Senior Machine Learning Data Pipeline Engineer

at Nvidia

USD 140,000-270,200 per year

SENIOR

✅ On-site

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI API @ 7 Data Engineering @ 6 Data Pipelines @ 7 Deep Learning @ 4 LLM @ 4 Machine Learning Mathematics @ 4 Python @ 7 Robotics @ 6 SQL @ 7

Details

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.

Responsibilities

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.

Requirements

Bachelor's or master's degree in Computer Science, Mathematics, Electrical Engineering, or a related quantitative field, or equivalent experience.
5+ years of experience in software or data engineering.
Strong Python and SQL skills demonstrated through production services, APIs, or data pipelines.
Hands-on experience developing and optimizing data solutions, including diagnosing DNN model performance and reliability issues.
Ability to develop and analyze automated 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.
Experience delivering data solutions from unclear requirements through production, including setting acceptance criteria with DNN engineers and data specialists and confirming outcomes.

Preferred Qualifications

Hands-on experience with foundation model data and training pipelines, including smaller-scale models built from scratch.
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.

Compensation and Benefits

The expected base salary ranges are $140,000–$224,250 USD for Level 3 and $168,000–$270,250 USD for Level 4. Base salary will be determined based on location, experience, and other objective job-related factors. The position also includes eligibility for equity and benefits.

Applications will be accepted at least until November 11, 2026. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and is an equal opportunity employer.