{"id":3905069,"url":"https://alion.io/job/nvidia-senior-machine-learning-data-pipeline-engineer","title":"Senior Machine Learning Data Pipeline Engineer","company":{"id":6,"name":"NVIDIA","domain":"nvidia.com","url":"https://alion.io/company/nvidia","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":83,"open_postings":794,"ghost_share":0.025,"stale_share":0.594,"repost_share":0.055,"time_to_fill_p50_days":41,"computed_at":"2026-11-08T05:47:12Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Santa Clara, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"VLM","optional":false}],"status":"live","first_seen_at":"2026-11-06T00:00:00Z","employer_posted_date":"2026-11-06","last_verified_at":"2026-11-08T20:35:34Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"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.\nYou 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.\nWhat you'll be doing:\nDesign, build, and own scalable data pipelines that automate data discovery, selection, and curation to meet DNN training data needs.\nDevelop 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.\nDeploy and operate these pipelines and workflows as reliable production systems, with workflow orchestration, monitoring, and failure recovery.\nPartner 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.\nWhat we need to see:\nBS 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. \nStrong Python and SQL skills, shown through production services, APIs, or data pipelines.\nHands-on development of data solutions and optimization, including diagnosing DNN model performance and reliability issues.\nAbility to develop and analyze automatic workflows that connect LLMs to tools and data services, including tool calling and workflow state management.\nSolid understanding of deep learning, embeddings, and LLM/VLM inference, including how training datasets are prepared and consumed by DNN teams.\nShown 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.\nWays to Stand Out from the Crowd:\nHands-on work on foundation model data and training pipelines, including smaller-scale models built from scratch.\nA track record of shipping LLM/VLM-based mining, multimodal retrieval, or curation services.\nBackground in autonomous driving or robotics data, including temporal video, sensor metadata, scenario definitions, or model failure analysis.\nYou will also be eligible for equity and benefits.\nApplications for this job will be accepted at least until November 10, 2026.This posting is for an existing vacancy.\nNVIDIA uses AI tools in its recruiting processes.\nNVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. 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