Senior Machine Learning Data Pipeline Engineer

We are hiring a Senior Machine Learning Data Pipeline Engineer to invent and build the next generation of data pipelines for end-to-end autonomous driving foundation models. You will develop scalable pipelines and agentic workflows that help DNN teams proactively identify data gaps and efficiently discover, prepare, and deliver the right training data.

You will convert model limitations and data needs into reusable engineering solutions, integrating data infrastructure with LLMs, vision-language models, and retrieval techniques to automate data solutions.

What you'll be doing:

• Design, build, and own scalable data pipelines that automate data discovery, selection, and curation to meet DNN training data needs.
• Develop agentic workflows that proactively identify model data needs, plan and implement mining tasks using data tools and model inference, and refine mining strategies through human review and downstream model feedback.
• Deploy and operate these pipelines and workflows as reliable production systems, with workflow orchestration, monitoring, and failure recovery.
• Partner with MLEs and data analysts to turn recurring data needs and model failure cases into reusable mining capabilities, define acceptance criteria, and improve scenario coverage through feedback.

What we need to see:

• BS or MS in Computer Science, Mathematics or Electrical Engineering, or a related quantitative field, or equivalent experience, plus 5+ years in software or data engineering.
• Strong Python and SQL skills, shown through production services, APIs, or data pipelines.
• Hands-on development of data solutions and optimization, including diagnosing DNN model performance and reliability issues.
• Ability to develop and analyze automatic workflows that connect LLMs to tools and data services, including tool calling and workflow state management.
• Solid understanding of deep learning, embeddings, and LLM/VLM inference, including how training datasets are prepared and consumed by DNN teams.
• Shown experience in delivering data solutions starting from unclear requirements through to production, involving setting acceptance criteria alongside DNN engineers and data specialists and confirming outcomes.

Ways to Stand Out from the Crowd:

• Hands-on work on foundation model data and training pipelines, including smaller-scale models built from scratch.
• A track record of shipping LLM/VLM-based mining, multimodal retrieval, or curation services.
• Background in autonomous driving or robotics data, including temporal video, sensor metadata, scenario definitions, or model failure analysis.

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This posting is for an existing vacancy. 

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