2 days ago
Responsibilities
- Design, build, and own scalable pipelines for data discovery, selection, curation, and delivery.
- Develop agentic workflows that identify model data needs, plan mining tasks, use data tools and model inference, and refine strategies through feedback.
- Deploy and operate reliable production pipelines with workflow orchestration, monitoring, and failure recovery.
- Partner with MLEs and data analysts to convert recurring data needs and model failures into reusable mining capabilities.
- Define acceptance criteria and improve scenario coverage through human review and downstream model feedback.
Requirements
- Bachelor’s or master’s degree in Computer Science, Mathematics, Electrical Engineering, or a related quantitative field, or equivalent experience.
- At least 5 years of software or data engineering experience.
- Strong Python and SQL skills demonstrated through production services, APIs, or data pipelines.
- Hands-on experience developing and optimizing data solutions and diagnosing DNN model performance and reliability issues.
- Ability to build workflows connecting LLMs with tools and data services, including tool calling and workflow state management.
- Solid understanding of deep learning, embeddings, LLM/VLM inference, and how training datasets are prepared and consumed.
- Experience delivering data solutions from ambiguous requirements through production while establishing acceptance criteria and confirming outcomes.
- Preferred experience with foundation-model data and training pipelines, smaller-scale models built from scratch, LLM/VLM-based mining, multimodal retrieval, curation services, autonomous driving or robotics data, temporal video, sensor metadata, scenario definitions, or model failure analysis.
Benefits
- Eligible for equity and benefits.
- Applications will be accepted at least until November 10, 2026.
- This posting is for an existing vacancy.
Categories
Data EngineeringML Engineering
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.