Lediga job som Civilingenjör i datateknik

Who are we?Volvo Cars is a company on a mission; to bring traditional car manufacturing into a connected, sustainable and smart future.Since 1927, we have been a brand known for our commitment to safety, creating innovative cars that make life less complicated for our consumers. In 2010, we decided to transform our business, resulting in a totally new generation of cars and technologies, as well as steady growth and record sales. Today, we’re expanding our global footprint in Europe, China and the US, and we’re on the lookout for new talent. We are constantly pushing our own skills and abilities to drive change in the automobile industry like never before. We are looking for innovative, committed people to join us in this endeavour and create safe, sustainable and connected cars. We believe in the power of people and will challenge and support you to reach your full potential. Join us and be part of Volvo Cars’ journey into the future.

Software Factory brings skilled professionals together to harmonize engineering workflows, build common tools, and accelerate software development at Volvo Cars. We develop and operate applications that turn engineering data into actionable software-quality KPIs, while building governed data products with clear ownership and reliable quality. Our insights help teams improve infrastructure use and make better decisions across the CI/CD flow.

We are looking for an experienced Data Engineer to help enable a stable, robust, and secure electrical system for our next-generation software and electronics platform.

As a Data Engineer, you will build and maintain scalable data platforms and ETL/ELT pipelines that support data-driven development across Software Factory. You will work with engineers and stakeholders to turn data needs into reliable solutions and ensure data is accessible, consistent, and fit for use.You will strengthen data governance by supporting standards, cataloguing assets, monitoring quality and compliance, and resolving data issues with data owners, analysts, and engineers. You will also promote sound data-management practices and ethical use of data.In this role, you will:• Design and maintain the end-to-end data platform, including ingestion, Apache Iceberg lakehouse storage, Dagster orchestration, and Snowflake warehousing• Build reusable ingestion frameworks for onboarding new data sources• Evolve lakehouse table formats, schemas, and governance• Define and enforce data contracts, service-level agreements, and quality standards• Monitor pipelines, data quality, and infrastructure health• Deliver data solutions for analytics and product use cases with cross-functional teams• Deploy and operate data infrastructure using Kubernetes, Helm, GitOps, and CI/CD• Document architecture, governance, and onboarding to support self-service data use• Evaluate technologies that improve platform capabilities

• MSc in Computer Science, Software Engineering, or equivalent practical experience• Strong Python and SQL skills• Experience building and operating data pipelines with orchestration tools such as Dagster or Airflow• Hands-on experience with Snowflake and lakehouse technologies such as Apache Iceberg, Delta Lake, or Hudi• Strong knowledge of data modelling and dbt• Experience with data cataloguing and governance tools such as Snowflake Open Catalog, Unity Catalog, or Polaris• Cloud-platform experience, preferably Azure, including Blob Storage, networking, and Entra ID• Experience deploying data services with Kubernetes, Helm, and Docker• Familiarity with CI/CD tools such as GitHub Actions• Experience with monitoring, alerting, and observability solutions such as Grafana• Experience working in agile environments such as Scrum, Kanban, or SAFeYou are structured, self-driven, and accountable, with strong collaboration and communication skills across disciplines.

Senior Full-Stack Engineer (ML & Data)

Own our prediction models from training pipeline to customer-facing feature. Strong applied ML — calibration, evals, leakage — plus full-stack TypeScript to ship it.

We build AI agents that audit and optimize e-commerce advertising. Our platform syncs live data from Google Ads, Meta, Merchant Center, and Google Analytics, runs it through a rule engine, LLM-powered audit agents, and our own prediction models, and turns findings into concrete actions marketers can apply with one click.

What sets us apart is measurement: we connect real profit data from our customers' e-commerce platforms to their ad accounts and build models that tell them what their marketing actually returns — not what ad platforms self-report. The ML in our product isn't decoration. Prediction models, calibration, and evaluation pipelines are core to what customers pay for.

We're a small team shipping fast on a modern, strictly-typed stack. You'd report directly to the CTO, with real ownership: features you design end-to-end, and models you own from training data to the number a customer sees on screen.

The role

This is a hybrid role — roughly 60% product engineering, 40% ML & data science. You'll ship full-stack features in our TypeScript monorepo and own prediction models as production software: framing the problem, building training and evaluation pipelines, monitoring calibration drift, and wiring outputs into the product.

We're not looking for a research scientist, or a pure web engineer. We want someone who treats a model the way a good engineer treats a service: tested, monitored, versioned, and honest about its failure modes.

What you'll do

• Own prediction models end-to-end — models that predict product returns and net profit per order — training data pipelines, feature engineering, evaluation (ranking quality and calibration), recalibration strategies, and serving model output into the product.
• Build and maintain data pipelines — SQL feature builders on PostgreSQL shared by training and serving, versioned dataset exports, backfills, and data quality checks that keep training data honest (no leakage, no silent schema drift).
• Design and build features across our TypeScript monorepo — tRPC procedures and NestJS services on the backend, React on the frontend — so model insights become actions marketers can apply with one click.
• Work on the AI audit agent — prompt design, rule evaluation, structured-output evals, and plumbing that turns LLM output into reliable, deduplicated actions across multiple model providers.
• Extend integration workers — sync campaign, product, and analytics data from Google and Meta APIs via Cloud Pub/Sub pipelines.
• Evolve the data model in PostgreSQL/Prisma — keep migrations, multi-tenancy, and role-based access clean as the product grows.
• Own quality — write tests (Jest on TypeScript, pytest on Python), review PRs, and keep our CI/CD pipeline to Cloud Run fast and boring.

Our stack

Backend — TypeScript, Node, js, NestJS + Fastify, tRPC, Zod, Prisma, PostgreSQL, ML & data — Python, pandas, scikit-learn, XGBoost, PyTorch, ONNX, SQL on PostgreSQL, BigQuery, Calibration pipelines, Bayesian MMM (Google Meridian)

Model serving — FastAPI on Cloud Run · Cloud Run Jobs · GPU training on GCP Batch

Frontend — React · Vite · Tailwind · End-to-end types via tRPC

Infrastructure — Google Cloud Run · Pub/Sub · Docker · Terraform · GitHub Actions · pnpm workspaces

AI & integrations — Multiple LLM providers · Google Ads · Meta · Merchant Center · GA4

What we're looking for

• 5+ years building production software, with strong TypeScript on both server and client — or strong TypeScript on one side plus deep Python, with the willingness to close the gap fast.
• Hands-on applied ML: you've trained, evaluated, and shipped prediction models that real users depended on. You can explain the difference between good ranking and good calibration, know what data leakage looks like, and have debugged a model that was confidently wrong.
• Fluent Python for data work (pandas, scikit-learn or similar) and strong SQL — comfortable with large datasets in PostgreSQL or BigQuery.
• Solid relational database instincts — schema design, migrations, and query performance in PostgreSQL (Prisma is a plus).
• Experience with event-driven or queue-based architectures (Pub/Sub, SQS, Kafka, or similar) and their failure modes.
• A habit of testing: you write tests because they let you move faster, not because someone told you to.
• Pragmatic product sense — you ask why before how, and you'd rather ship a focused, well-measured model than a state-of-the-art one nobody can maintain.

Nice to have

• Bayesian modeling or marketing mix modeling experience (priors, ROI calibration, health diagnostics).
• Hands-on work with LLM APIs in production — prompting, evals, structured output, cost control, switching providers.
• Experience serving models on GCP (Cloud Run, GCP Batch, Vertex AI) and monitoring for drift.
• Familiarity with Google Ads, Meta, or Merchant Center APIs, or ad-tech / e-commerce domain experience.
• NestJS, tRPC, or GCP (Cloud Run, Artifact Registry, Workload Identity) in a previous role.

How we work

Small team, short feedback loops, no ceremony for ceremony's sake. Strict TypeScript, code review on everything, CI that deploys to Cloud Run on merge. Models are held to the same standard as code: evaluated before they ship, monitored after. We optimize for shipping value weekly and keeping the codebase a place people enjoy working in.

We're a fast-growing team building AI agents that handle the repetitive work — so companies can focus on vision, strategy, and innovation. Here, you'll own ideas from first sketch to finished product, work closely with the founders, and see your impact immediately. We celebrate success, learn from challenges, and believe true magic happens through collaboration.

Want to shape the future of AI-driven marketing?

Interviews are conducted on an ongoing basis, so please submit your application as soon as possible.

You will be employed directly by the company. You will receive information about which company the advertisement refers to when you are in contact with the recruiter. Bumbli Group is therefore not the employer but the recruitment agency.

Welcome to submit your application!

Bumbli Group is part of House of Recruitment — a personal, traditional and quality-driven recruitment agency in Gothenburg.

Who are we?Volvo Cars is a company on a mission; to bring traditional car manufacturing into a connected, sustainable and smart future.Since 1927, we have been a brand known for our commitment to safety, creating innovative cars that make life less complicated for our consumers. In 2010, we decided to transform our business, resulting in a totally new generation of cars and technologies, as well as steady growth and record sales. Today, we’re expanding our global footprint in Europe, China and the US, and we’re on the lookout for new talent. We are constantly pushing our own skills and abilities to drive change in the automobile industry like never before. We are looking for innovative, committed people to join us in this endeavour and create safe, sustainable and connected cars. We believe in the power of people and will challenge and support you to reach your full potential. Join us and be part of Volvo Cars’ journey into the future.

Software Factory brings skilled professionals together to harmonize engineering workflows, build common tools, and accelerate software development at Volvo Cars. We develop and operate applications that turn engineering data into actionable software-quality KPIs, while building governed data products with clear ownership and reliable quality. Our insights help teams improve infrastructure use and make better decisions across the CI/CD flow.

We are looking for an experienced Data Engineer to help enable a stable, robust, and secure electrical system for our next-generation software and electronics platform.

As a Data Engineer, you will build and maintain scalable data platforms and ETL/ELT pipelines that support data-driven development across Software Factory. You will work with engineers and stakeholders to turn data needs into reliable solutions and ensure data is accessible, consistent, and fit for use.You will strengthen data governance by supporting standards, cataloguing assets, monitoring quality and compliance, and resolving data issues with data owners, analysts, and engineers. You will also promote sound data-management practices and ethical use of data.In this role, you will:• Design and maintain the end-to-end data platform, including ingestion, Apache Iceberg lakehouse storage, Dagster orchestration, and Snowflake warehousing• Build reusable ingestion frameworks for onboarding new data sources• Evolve lakehouse table formats, schemas, and governance• Define and enforce data contracts, service-level agreements, and quality standards• Monitor pipelines, data quality, and infrastructure health• Deliver data solutions for analytics and product use cases with cross-functional teams• Deploy and operate data infrastructure using Kubernetes, Helm, GitOps, and CI/CD• Document architecture, governance, and onboarding to support self-service data use• Evaluate technologies that improve platform capabilities

• MSc in Computer Science, Software Engineering, or equivalent practical experience• Strong Python and SQL skills• Experience building and operating data pipelines with orchestration tools such as Dagster or Airflow• Hands-on experience with Snowflake and lakehouse technologies such as Apache Iceberg, Delta Lake, or Hudi• Strong knowledge of data modelling and dbt• Experience with data cataloguing and governance tools such as Snowflake Open Catalog, Unity Catalog, or Polaris• Cloud-platform experience, preferably Azure, including Blob Storage, networking, and Entra ID• Experience deploying data services with Kubernetes, Helm, and Docker• Familiarity with CI/CD tools such as GitHub Actions• Experience with monitoring, alerting, and observability solutions such as Grafana• Experience working in agile environments such as Scrum, Kanban, or SAFeYou are structured, self-driven, and accountable, with strong collaboration and communication skills across disciplines.

Senior Full-Stack Engineer (ML & Data)

Own our prediction models from training pipeline to customer-facing feature. Strong applied ML — calibration, evals, leakage — plus full-stack TypeScript to ship it.

We build AI agents that audit and optimize e-commerce advertising. Our platform syncs live data from Google Ads, Meta, Merchant Center, and Google Analytics, runs it through a rule engine, LLM-powered audit agents, and our own prediction models, and turns findings into concrete actions marketers can apply with one click.

What sets us apart is measurement: we connect real profit data from our customers' e-commerce platforms to their ad accounts and build models that tell them what their marketing actually returns — not what ad platforms self-report. The ML in our product isn't decoration. Prediction models, calibration, and evaluation pipelines are core to what customers pay for.

We're a small team shipping fast on a modern, strictly-typed stack. You'd report directly to the CTO, with real ownership: features you design end-to-end, and models you own from training data to the number a customer sees on screen.

The role

This is a hybrid role — roughly 60% product engineering, 40% ML & data science. You'll ship full-stack features in our TypeScript monorepo and own prediction models as production software: framing the problem, building training and evaluation pipelines, monitoring calibration drift, and wiring outputs into the product.

We're not looking for a research scientist, or a pure web engineer. We want someone who treats a model the way a good engineer treats a service: tested, monitored, versioned, and honest about its failure modes.

What you'll do

• Own prediction models end-to-end — models that predict product returns and net profit per order — training data pipelines, feature engineering, evaluation (ranking quality and calibration), recalibration strategies, and serving model output into the product.
• Build and maintain data pipelines — SQL feature builders on PostgreSQL shared by training and serving, versioned dataset exports, backfills, and data quality checks that keep training data honest (no leakage, no silent schema drift).
• Design and build features across our TypeScript monorepo — tRPC procedures and NestJS services on the backend, React on the frontend — so model insights become actions marketers can apply with one click.
• Work on the AI audit agent — prompt design, rule evaluation, structured-output evals, and plumbing that turns LLM output into reliable, deduplicated actions across multiple model providers.
• Extend integration workers — sync campaign, product, and analytics data from Google and Meta APIs via Cloud Pub/Sub pipelines.
• Evolve the data model in PostgreSQL/Prisma — keep migrations, multi-tenancy, and role-based access clean as the product grows.
• Own quality — write tests (Jest on TypeScript, pytest on Python), review PRs, and keep our CI/CD pipeline to Cloud Run fast and boring.

Our stack

Backend — TypeScript, Node, js, NestJS + Fastify, tRPC, Zod, Prisma, PostgreSQL, ML & data — Python, pandas, scikit-learn, XGBoost, PyTorch, ONNX, SQL on PostgreSQL, BigQuery, Calibration pipelines, Bayesian MMM (Google Meridian)

Model serving — FastAPI on Cloud Run · Cloud Run Jobs · GPU training on GCP Batch

Frontend — React · Vite · Tailwind · End-to-end types via tRPC

Infrastructure — Google Cloud Run · Pub/Sub · Docker · Terraform · GitHub Actions · pnpm workspaces

AI & integrations — Multiple LLM providers · Google Ads · Meta · Merchant Center · GA4

What we're looking for

• 5+ years building production software, with strong TypeScript on both server and client — or strong TypeScript on one side plus deep Python, with the willingness to close the gap fast.
• Hands-on applied ML: you've trained, evaluated, and shipped prediction models that real users depended on. You can explain the difference between good ranking and good calibration, know what data leakage looks like, and have debugged a model that was confidently wrong.
• Fluent Python for data work (pandas, scikit-learn or similar) and strong SQL — comfortable with large datasets in PostgreSQL or BigQuery.
• Solid relational database instincts — schema design, migrations, and query performance in PostgreSQL (Prisma is a plus).
• Experience with event-driven or queue-based architectures (Pub/Sub, SQS, Kafka, or similar) and their failure modes.
• A habit of testing: you write tests because they let you move faster, not because someone told you to.
• Pragmatic product sense — you ask why before how, and you'd rather ship a focused, well-measured model than a state-of-the-art one nobody can maintain.

Nice to have

• Bayesian modeling or marketing mix modeling experience (priors, ROI calibration, health diagnostics).
• Hands-on work with LLM APIs in production — prompting, evals, structured output, cost control, switching providers.
• Experience serving models on GCP (Cloud Run, GCP Batch, Vertex AI) and monitoring for drift.
• Familiarity with Google Ads, Meta, or Merchant Center APIs, or ad-tech / e-commerce domain experience.
• NestJS, tRPC, or GCP (Cloud Run, Artifact Registry, Workload Identity) in a previous role.

How we work

Small team, short feedback loops, no ceremony for ceremony's sake. Strict TypeScript, code review on everything, CI that deploys to Cloud Run on merge. Models are held to the same standard as code: evaluated before they ship, monitored after. We optimize for shipping value weekly and keeping the codebase a place people enjoy working in.

We're a fast-growing team building AI agents that handle the repetitive work — so companies can focus on vision, strategy, and innovation. Here, you'll own ideas from first sketch to finished product, work closely with the founders, and see your impact immediately. We celebrate success, learn from challenges, and believe true magic happens through collaboration.

Want to shape the future of AI-driven marketing?

Interviews are conducted on an ongoing basis, so please submit your application as soon as possible.

You will be employed directly by the company. You will receive information about which company the advertisement refers to when you are in contact with the recruiter. Bumbli Group is therefore not the employer but the recruitment agency.

Welcome to submit your application!

Bumbli Group is part of House of Recruitment — a personal, traditional and quality-driven recruitment agency in Gothenburg.

Who are we?Volvo Cars is a company on a mission; to bring traditional car manufacturing into a connected, sustainable and smart future.Since 1927, we have been a brand known for our commitment to safety, creating innovative cars that make life less complicated for our consumers. In 2010, we decided to transform our business, resulting in a totally new generation of cars and technologies, as well as steady growth and record sales. Today, we’re expanding our global footprint in Europe, China and the US, and we’re on the lookout for new talent. We are constantly pushing our own skills and abilities to drive change in the automobile industry like never before. We are looking for innovative, committed people to join us in this endeavour and create safe, sustainable and connected cars. We believe in the power of people and will challenge and support you to reach your full potential. Join us and be part of Volvo Cars’ journey into the future.

Software Factory brings skilled professionals together to harmonize engineering workflows, build common tools, and accelerate software development at Volvo Cars. We develop and operate applications that turn engineering data into actionable software-quality KPIs, while building governed data products with clear ownership and reliable quality. Our insights help teams improve infrastructure use and make better decisions across the CI/CD flow.

We are looking for an experienced Data Engineer to help enable a stable, robust, and secure electrical system for our next-generation software and electronics platform.

As a Data Engineer, you will build and maintain scalable data platforms and ETL/ELT pipelines that support data-driven development across Software Factory. You will work with engineers and stakeholders to turn data needs into reliable solutions and ensure data is accessible, consistent, and fit for use.You will strengthen data governance by supporting standards, cataloguing assets, monitoring quality and compliance, and resolving data issues with data owners, analysts, and engineers. You will also promote sound data-management practices and ethical use of data.In this role, you will:• Design and maintain the end-to-end data platform, including ingestion, Apache Iceberg lakehouse storage, Dagster orchestration, and Snowflake warehousing• Build reusable ingestion frameworks for onboarding new data sources• Evolve lakehouse table formats, schemas, and governance• Define and enforce data contracts, service-level agreements, and quality standards• Monitor pipelines, data quality, and infrastructure health• Deliver data solutions for analytics and product use cases with cross-functional teams• Deploy and operate data infrastructure using Kubernetes, Helm, GitOps, and CI/CD• Document architecture, governance, and onboarding to support self-service data use• Evaluate technologies that improve platform capabilities

• MSc in Computer Science, Software Engineering, or equivalent practical experience• Strong Python and SQL skills• Experience building and operating data pipelines with orchestration tools such as Dagster or Airflow• Hands-on experience with Snowflake and lakehouse technologies such as Apache Iceberg, Delta Lake, or Hudi• Strong knowledge of data modelling and dbt• Experience with data cataloguing and governance tools such as Snowflake Open Catalog, Unity Catalog, or Polaris• Cloud-platform experience, preferably Azure, including Blob Storage, networking, and Entra ID• Experience deploying data services with Kubernetes, Helm, and Docker• Familiarity with CI/CD tools such as GitHub Actions• Experience with monitoring, alerting, and observability solutions such as Grafana• Experience working in agile environments such as Scrum, Kanban, or SAFeYou are structured, self-driven, and accountable, with strong collaboration and communication skills across disciplines.

Senior Full-Stack Engineer (ML & Data)

Own our prediction models from training pipeline to customer-facing feature. Strong applied ML — calibration, evals, leakage — plus full-stack TypeScript to ship it.

We build AI agents that audit and optimize e-commerce advertising. Our platform syncs live data from Google Ads, Meta, Merchant Center, and Google Analytics, runs it through a rule engine, LLM-powered audit agents, and our own prediction models, and turns findings into concrete actions marketers can apply with one click.

What sets us apart is measurement: we connect real profit data from our customers' e-commerce platforms to their ad accounts and build models that tell them what their marketing actually returns — not what ad platforms self-report. The ML in our product isn't decoration. Prediction models, calibration, and evaluation pipelines are core to what customers pay for.

We're a small team shipping fast on a modern, strictly-typed stack. You'd report directly to the CTO, with real ownership: features you design end-to-end, and models you own from training data to the number a customer sees on screen.

The role

This is a hybrid role — roughly 60% product engineering, 40% ML & data science. You'll ship full-stack features in our TypeScript monorepo and own prediction models as production software: framing the problem, building training and evaluation pipelines, monitoring calibration drift, and wiring outputs into the product.

We're not looking for a research scientist, or a pure web engineer. We want someone who treats a model the way a good engineer treats a service: tested, monitored, versioned, and honest about its failure modes.

What you'll do

• Own prediction models end-to-end — models that predict product returns and net profit per order — training data pipelines, feature engineering, evaluation (ranking quality and calibration), recalibration strategies, and serving model output into the product.
• Build and maintain data pipelines — SQL feature builders on PostgreSQL shared by training and serving, versioned dataset exports, backfills, and data quality checks that keep training data honest (no leakage, no silent schema drift).
• Design and build features across our TypeScript monorepo — tRPC procedures and NestJS services on the backend, React on the frontend — so model insights become actions marketers can apply with one click.
• Work on the AI audit agent — prompt design, rule evaluation, structured-output evals, and plumbing that turns LLM output into reliable, deduplicated actions across multiple model providers.
• Extend integration workers — sync campaign, product, and analytics data from Google and Meta APIs via Cloud Pub/Sub pipelines.
• Evolve the data model in PostgreSQL/Prisma — keep migrations, multi-tenancy, and role-based access clean as the product grows.
• Own quality — write tests (Jest on TypeScript, pytest on Python), review PRs, and keep our CI/CD pipeline to Cloud Run fast and boring.

Our stack

Backend — TypeScript, Node, js, NestJS + Fastify, tRPC, Zod, Prisma, PostgreSQL, ML & data — Python, pandas, scikit-learn, XGBoost, PyTorch, ONNX, SQL on PostgreSQL, BigQuery, Calibration pipelines, Bayesian MMM (Google Meridian)

Model serving — FastAPI on Cloud Run · Cloud Run Jobs · GPU training on GCP Batch

Frontend — React · Vite · Tailwind · End-to-end types via tRPC

Infrastructure — Google Cloud Run · Pub/Sub · Docker · Terraform · GitHub Actions · pnpm workspaces

AI & integrations — Multiple LLM providers · Google Ads · Meta · Merchant Center · GA4

What we're looking for

• 5+ years building production software, with strong TypeScript on both server and client — or strong TypeScript on one side plus deep Python, with the willingness to close the gap fast.
• Hands-on applied ML: you've trained, evaluated, and shipped prediction models that real users depended on. You can explain the difference between good ranking and good calibration, know what data leakage looks like, and have debugged a model that was confidently wrong.
• Fluent Python for data work (pandas, scikit-learn or similar) and strong SQL — comfortable with large datasets in PostgreSQL or BigQuery.
• Solid relational database instincts — schema design, migrations, and query performance in PostgreSQL (Prisma is a plus).
• Experience with event-driven or queue-based architectures (Pub/Sub, SQS, Kafka, or similar) and their failure modes.
• A habit of testing: you write tests because they let you move faster, not because someone told you to.
• Pragmatic product sense — you ask why before how, and you'd rather ship a focused, well-measured model than a state-of-the-art one nobody can maintain.

Nice to have

• Bayesian modeling or marketing mix modeling experience (priors, ROI calibration, health diagnostics).
• Hands-on work with LLM APIs in production — prompting, evals, structured output, cost control, switching providers.
• Experience serving models on GCP (Cloud Run, GCP Batch, Vertex AI) and monitoring for drift.
• Familiarity with Google Ads, Meta, or Merchant Center APIs, or ad-tech / e-commerce domain experience.
• NestJS, tRPC, or GCP (Cloud Run, Artifact Registry, Workload Identity) in a previous role.

How we work

Small team, short feedback loops, no ceremony for ceremony's sake. Strict TypeScript, code review on everything, CI that deploys to Cloud Run on merge. Models are held to the same standard as code: evaluated before they ship, monitored after. We optimize for shipping value weekly and keeping the codebase a place people enjoy working in.

We're a fast-growing team building AI agents that handle the repetitive work — so companies can focus on vision, strategy, and innovation. Here, you'll own ideas from first sketch to finished product, work closely with the founders, and see your impact immediately. We celebrate success, learn from challenges, and believe true magic happens through collaboration.

Want to shape the future of AI-driven marketing?

Interviews are conducted on an ongoing basis, so please submit your application as soon as possible.

You will be employed directly by the company. You will receive information about which company the advertisement refers to when you are in contact with the recruiter. Bumbli Group is therefore not the employer but the recruitment agency.

Welcome to submit your application!

Bumbli Group is part of House of Recruitment — a personal, traditional and quality-driven recruitment agency in Gothenburg.

Who are we?Volvo Cars is a company on a mission; to bring traditional car manufacturing into a connected, sustainable and smart future.Since 1927, we have been a brand known for our commitment to safety, creating innovative cars that make life less complicated for our consumers. In 2010, we decided to transform our business, resulting in a totally new generation of cars and technologies, as well as steady growth and record sales. Today, we’re expanding our global footprint in Europe, China and the US, and we’re on the lookout for new talent. We are constantly pushing our own skills and abilities to drive change in the automobile industry like never before. We are looking for innovative, committed people to join us in this endeavour and create safe, sustainable and connected cars. We believe in the power of people and will challenge and support you to reach your full potential. Join us and be part of Volvo Cars’ journey into the future.

Software Factory brings skilled professionals together to harmonize engineering workflows, build common tools, and accelerate software development at Volvo Cars. We develop and operate applications that turn engineering data into actionable software-quality KPIs, while building governed data products with clear ownership and reliable quality. Our insights help teams improve infrastructure use and make better decisions across the CI/CD flow.

We are looking for an experienced Data Engineer to help enable a stable, robust, and secure electrical system for our next-generation software and electronics platform.

As a Data Engineer, you will build and maintain scalable data platforms and ETL/ELT pipelines that support data-driven development across Software Factory. You will work with engineers and stakeholders to turn data needs into reliable solutions and ensure data is accessible, consistent, and fit for use.You will strengthen data governance by supporting standards, cataloguing assets, monitoring quality and compliance, and resolving data issues with data owners, analysts, and engineers. You will also promote sound data-management practices and ethical use of data.In this role, you will:• Design and maintain the end-to-end data platform, including ingestion, Apache Iceberg lakehouse storage, Dagster orchestration, and Snowflake warehousing• Build reusable ingestion frameworks for onboarding new data sources• Evolve lakehouse table formats, schemas, and governance• Define and enforce data contracts, service-level agreements, and quality standards• Monitor pipelines, data quality, and infrastructure health• Deliver data solutions for analytics and product use cases with cross-functional teams• Deploy and operate data infrastructure using Kubernetes, Helm, GitOps, and CI/CD• Document architecture, governance, and onboarding to support self-service data use• Evaluate technologies that improve platform capabilities

• MSc in Computer Science, Software Engineering, or equivalent practical experience• Strong Python and SQL skills• Experience building and operating data pipelines with orchestration tools such as Dagster or Airflow• Hands-on experience with Snowflake and lakehouse technologies such as Apache Iceberg, Delta Lake, or Hudi• Strong knowledge of data modelling and dbt• Experience with data cataloguing and governance tools such as Snowflake Open Catalog, Unity Catalog, or Polaris• Cloud-platform experience, preferably Azure, including Blob Storage, networking, and Entra ID• Experience deploying data services with Kubernetes, Helm, and Docker• Familiarity with CI/CD tools such as GitHub Actions• Experience with monitoring, alerting, and observability solutions such as Grafana• Experience working in agile environments such as Scrum, Kanban, or SAFeYou are structured, self-driven, and accountable, with strong collaboration and communication skills across disciplines.

Senior Full-Stack Engineer (ML & Data)

Own our prediction models from training pipeline to customer-facing feature. Strong applied ML — calibration, evals, leakage — plus full-stack TypeScript to ship it.

We build AI agents that audit and optimize e-commerce advertising. Our platform syncs live data from Google Ads, Meta, Merchant Center, and Google Analytics, runs it through a rule engine, LLM-powered audit agents, and our own prediction models, and turns findings into concrete actions marketers can apply with one click.

What sets us apart is measurement: we connect real profit data from our customers' e-commerce platforms to their ad accounts and build models that tell them what their marketing actually returns — not what ad platforms self-report. The ML in our product isn't decoration. Prediction models, calibration, and evaluation pipelines are core to what customers pay for.

We're a small team shipping fast on a modern, strictly-typed stack. You'd report directly to the CTO, with real ownership: features you design end-to-end, and models you own from training data to the number a customer sees on screen.

The role

This is a hybrid role — roughly 60% product engineering, 40% ML & data science. You'll ship full-stack features in our TypeScript monorepo and own prediction models as production software: framing the problem, building training and evaluation pipelines, monitoring calibration drift, and wiring outputs into the product.

We're not looking for a research scientist, or a pure web engineer. We want someone who treats a model the way a good engineer treats a service: tested, monitored, versioned, and honest about its failure modes.

What you'll do

• Own prediction models end-to-end — models that predict product returns and net profit per order — training data pipelines, feature engineering, evaluation (ranking quality and calibration), recalibration strategies, and serving model output into the product.
• Build and maintain data pipelines — SQL feature builders on PostgreSQL shared by training and serving, versioned dataset exports, backfills, and data quality checks that keep training data honest (no leakage, no silent schema drift).
• Design and build features across our TypeScript monorepo — tRPC procedures and NestJS services on the backend, React on the frontend — so model insights become actions marketers can apply with one click.
• Work on the AI audit agent — prompt design, rule evaluation, structured-output evals, and plumbing that turns LLM output into reliable, deduplicated actions across multiple model providers.
• Extend integration workers — sync campaign, product, and analytics data from Google and Meta APIs via Cloud Pub/Sub pipelines.
• Evolve the data model in PostgreSQL/Prisma — keep migrations, multi-tenancy, and role-based access clean as the product grows.
• Own quality — write tests (Jest on TypeScript, pytest on Python), review PRs, and keep our CI/CD pipeline to Cloud Run fast and boring.

Our stack

Backend — TypeScript, Node, js, NestJS + Fastify, tRPC, Zod, Prisma, PostgreSQL, ML & data — Python, pandas, scikit-learn, XGBoost, PyTorch, ONNX, SQL on PostgreSQL, BigQuery, Calibration pipelines, Bayesian MMM (Google Meridian)

Model serving — FastAPI on Cloud Run · Cloud Run Jobs · GPU training on GCP Batch

Frontend — React · Vite · Tailwind · End-to-end types via tRPC

Infrastructure — Google Cloud Run · Pub/Sub · Docker · Terraform · GitHub Actions · pnpm workspaces

AI & integrations — Multiple LLM providers · Google Ads · Meta · Merchant Center · GA4

What we're looking for

• 5+ years building production software, with strong TypeScript on both server and client — or strong TypeScript on one side plus deep Python, with the willingness to close the gap fast.
• Hands-on applied ML: you've trained, evaluated, and shipped prediction models that real users depended on. You can explain the difference between good ranking and good calibration, know what data leakage looks like, and have debugged a model that was confidently wrong.
• Fluent Python for data work (pandas, scikit-learn or similar) and strong SQL — comfortable with large datasets in PostgreSQL or BigQuery.
• Solid relational database instincts — schema design, migrations, and query performance in PostgreSQL (Prisma is a plus).
• Experience with event-driven or queue-based architectures (Pub/Sub, SQS, Kafka, or similar) and their failure modes.
• A habit of testing: you write tests because they let you move faster, not because someone told you to.
• Pragmatic product sense — you ask why before how, and you'd rather ship a focused, well-measured model than a state-of-the-art one nobody can maintain.

Nice to have

• Bayesian modeling or marketing mix modeling experience (priors, ROI calibration, health diagnostics).
• Hands-on work with LLM APIs in production — prompting, evals, structured output, cost control, switching providers.
• Experience serving models on GCP (Cloud Run, GCP Batch, Vertex AI) and monitoring for drift.
• Familiarity with Google Ads, Meta, or Merchant Center APIs, or ad-tech / e-commerce domain experience.
• NestJS, tRPC, or GCP (Cloud Run, Artifact Registry, Workload Identity) in a previous role.

How we work

Small team, short feedback loops, no ceremony for ceremony's sake. Strict TypeScript, code review on everything, CI that deploys to Cloud Run on merge. Models are held to the same standard as code: evaluated before they ship, monitored after. We optimize for shipping value weekly and keeping the codebase a place people enjoy working in.

We're a fast-growing team building AI agents that handle the repetitive work — so companies can focus on vision, strategy, and innovation. Here, you'll own ideas from first sketch to finished product, work closely with the founders, and see your impact immediately. We celebrate success, learn from challenges, and believe true magic happens through collaboration.

Want to shape the future of AI-driven marketing?

Interviews are conducted on an ongoing basis, so please submit your application as soon as possible.

You will be employed directly by the company. You will receive information about which company the advertisement refers to when you are in contact with the recruiter. Bumbli Group is therefore not the employer but the recruitment agency.

Welcome to submit your application!

Bumbli Group is part of House of Recruitment — a personal, traditional and quality-driven recruitment agency in Gothenburg.

Senior Full-Stack Engineer (ML & Data)

Own our prediction models from training pipeline to customer-facing feature. Strong applied ML — calibration, evals, leakage — plus full-stack TypeScript to ship it.

We build AI agents that audit and optimize e-commerce advertising. Our platform syncs live data from Google Ads, Meta, Merchant Center, and Google Analytics, runs it through a rule engine, LLM-powered audit agents, and our own prediction models, and turns findings into concrete actions marketers can apply with one click.

What sets us apart is measurement: we connect real profit data from our customers' e-commerce platforms to their ad accounts and build models that tell them what their marketing actually returns — not what ad platforms self-report. The ML in our product isn't decoration. Prediction models, calibration, and evaluation pipelines are core to what customers pay for.

We're a small team shipping fast on a modern, strictly-typed stack. You'd report directly to the CTO, with real ownership: features you design end-to-end, and models you own from training data to the number a customer sees on screen.

The role

This is a hybrid role — roughly 60% product engineering, 40% ML & data science. You'll ship full-stack features in our TypeScript monorepo and own prediction models as production software: framing the problem, building training and evaluation pipelines, monitoring calibration drift, and wiring outputs into the product.

We're not looking for a research scientist, or a pure web engineer. We want someone who treats a model the way a good engineer treats a service: tested, monitored, versioned, and honest about its failure modes.

What you'll do

• Own prediction models end-to-end — models that predict product returns and net profit per order — training data pipelines, feature engineering, evaluation (ranking quality and calibration), recalibration strategies, and serving model output into the product.
• Build and maintain data pipelines — SQL feature builders on PostgreSQL shared by training and serving, versioned dataset exports, backfills, and data quality checks that keep training data honest (no leakage, no silent schema drift).
• Design and build features across our TypeScript monorepo — tRPC procedures and NestJS services on the backend, React on the frontend — so model insights become actions marketers can apply with one click.
• Work on the AI audit agent — prompt design, rule evaluation, structured-output evals, and plumbing that turns LLM output into reliable, deduplicated actions across multiple model providers.
• Extend integration workers — sync campaign, product, and analytics data from Google and Meta APIs via Cloud Pub/Sub pipelines.
• Evolve the data model in PostgreSQL/Prisma — keep migrations, multi-tenancy, and role-based access clean as the product grows.
• Own quality — write tests (Jest on TypeScript, pytest on Python), review PRs, and keep our CI/CD pipeline to Cloud Run fast and boring.

Our stack

Backend — TypeScript, Node, js, NestJS + Fastify, tRPC, Zod, Prisma, PostgreSQL, ML & data — Python, pandas, scikit-learn, XGBoost, PyTorch, ONNX, SQL on PostgreSQL, BigQuery, Calibration pipelines, Bayesian MMM (Google Meridian)

Model serving — FastAPI on Cloud Run · Cloud Run Jobs · GPU training on GCP Batch

Frontend — React · Vite · Tailwind · End-to-end types via tRPC

Infrastructure — Google Cloud Run · Pub/Sub · Docker · Terraform · GitHub Actions · pnpm workspaces

AI & integrations — Multiple LLM providers · Google Ads · Meta · Merchant Center · GA4

What we're looking for

• 5+ years building production software, with strong TypeScript on both server and client — or strong TypeScript on one side plus deep Python, with the willingness to close the gap fast.
• Hands-on applied ML: you've trained, evaluated, and shipped prediction models that real users depended on. You can explain the difference between good ranking and good calibration, know what data leakage looks like, and have debugged a model that was confidently wrong.
• Fluent Python for data work (pandas, scikit-learn or similar) and strong SQL — comfortable with large datasets in PostgreSQL or BigQuery.
• Solid relational database instincts — schema design, migrations, and query performance in PostgreSQL (Prisma is a plus).
• Experience with event-driven or queue-based architectures (Pub/Sub, SQS, Kafka, or similar) and their failure modes.
• A habit of testing: you write tests because they let you move faster, not because someone told you to.
• Pragmatic product sense — you ask why before how, and you'd rather ship a focused, well-measured model than a state-of-the-art one nobody can maintain.

Nice to have

• Bayesian modeling or marketing mix modeling experience (priors, ROI calibration, health diagnostics).
• Hands-on work with LLM APIs in production — prompting, evals, structured output, cost control, switching providers.
• Experience serving models on GCP (Cloud Run, GCP Batch, Vertex AI) and monitoring for drift.
• Familiarity with Google Ads, Meta, or Merchant Center APIs, or ad-tech / e-commerce domain experience.
• NestJS, tRPC, or GCP (Cloud Run, Artifact Registry, Workload Identity) in a previous role.

How we work

Small team, short feedback loops, no ceremony for ceremony's sake. Strict TypeScript, code review on everything, CI that deploys to Cloud Run on merge. Models are held to the same standard as code: evaluated before they ship, monitored after. We optimize for shipping value weekly and keeping the codebase a place people enjoy working in.

We're a fast-growing team building AI agents that handle the repetitive work — so companies can focus on vision, strategy, and innovation. Here, you'll own ideas from first sketch to finished product, work closely with the founders, and see your impact immediately. We celebrate success, learn from challenges, and believe true magic happens through collaboration.

Want to shape the future of AI-driven marketing?

Interviews are conducted on an ongoing basis, so please submit your application as soon as possible.

You will be employed directly by the company. You will receive information about which company the advertisement refers to when you are in contact with the recruiter. Bumbli Group is therefore not the employer but the recruitment agency.

Welcome to submit your application!

Bumbli Group is part of House of Recruitment — a personal, traditional and quality-driven recruitment agency in Gothenburg.

Who are we?Volvo Cars is a company on a mission; to bring traditional car manufacturing into a connected, sustainable and smart future.Since 1927, we have been a brand known for our commitment to safety, creating innovative cars that make life less complicated for our consumers. In 2010, we decided to transform our business, resulting in a totally new generation of cars and technologies, as well as steady growth and record sales. Today, we’re expanding our global footprint in Europe, China and the US, and we’re on the lookout for new talent. We are constantly pushing our own skills and abilities to drive change in the automobile industry like never before. We are looking for innovative, committed people to join us in this endeavour and create safe, sustainable and connected cars. We believe in the power of people and will challenge and support you to reach your full potential. Join us and be part of Volvo Cars’ journey into the future.

Software Factory brings skilled professionals together to harmonize engineering workflows, build common tools, and accelerate software development at Volvo Cars. We develop and operate applications that turn engineering data into actionable software-quality KPIs, while building governed data products with clear ownership and reliable quality. Our insights help teams improve infrastructure use and make better decisions across the CI/CD flow.

We are looking for an experienced Data Engineer to help enable a stable, robust, and secure electrical system for our next-generation software and electronics platform.

As a Data Engineer, you will build and maintain scalable data platforms and ETL/ELT pipelines that support data-driven development across Software Factory. You will work with engineers and stakeholders to turn data needs into reliable solutions and ensure data is accessible, consistent, and fit for use.You will strengthen data governance by supporting standards, cataloguing assets, monitoring quality and compliance, and resolving data issues with data owners, analysts, and engineers. You will also promote sound data-management practices and ethical use of data.In this role, you will:• Design and maintain the end-to-end data platform, including ingestion, Apache Iceberg lakehouse storage, Dagster orchestration, and Snowflake warehousing• Build reusable ingestion frameworks for onboarding new data sources• Evolve lakehouse table formats, schemas, and governance• Define and enforce data contracts, service-level agreements, and quality standards• Monitor pipelines, data quality, and infrastructure health• Deliver data solutions for analytics and product use cases with cross-functional teams• Deploy and operate data infrastructure using Kubernetes, Helm, GitOps, and CI/CD• Document architecture, governance, and onboarding to support self-service data use• Evaluate technologies that improve platform capabilities

• MSc in Computer Science, Software Engineering, or equivalent practical experience• Strong Python and SQL skills• Experience building and operating data pipelines with orchestration tools such as Dagster or Airflow• Hands-on experience with Snowflake and lakehouse technologies such as Apache Iceberg, Delta Lake, or Hudi• Strong knowledge of data modelling and dbt• Experience with data cataloguing and governance tools such as Snowflake Open Catalog, Unity Catalog, or Polaris• Cloud-platform experience, preferably Azure, including Blob Storage, networking, and Entra ID• Experience deploying data services with Kubernetes, Helm, and Docker• Familiarity with CI/CD tools such as GitHub Actions• Experience with monitoring, alerting, and observability solutions such as Grafana• Experience working in agile environments such as Scrum, Kanban, or SAFeYou are structured, self-driven, and accountable, with strong collaboration and communication skills across disciplines.