Senior Research Engineer - AI Coding Tools
$224k - $431k • US, CA, Santa Clara
Posted 1d ago
Job Location
US, CA, Santa Clara
Tech Stack
Remote Work Policy
On-site
Categories
AI Agent Engineer
About the job
NVIDIA is seeking a Senior Research Engineer to join their AI Developer Tools organization. This role focuses on building AI coding agents, models, datasets, and evaluations to empower CUDA developers with AI-powered coding tools. The team operates at the forefront of AI development, working on novel coding agents, fine-tuning code LLMs, publishing benchmarks, and contributing datasets for foundation models. This position offers the opportunity to apply AI research depth to a fast-paced, shipping-oriented team, influencing new projects and collaborating with product teams to integrate research into features used by both internal and external customers.
Responsibilities
- Build and improve novel coding agents for NVIDIA developers to write, optimize, and maintain CUDA code, ensuring compatibility with other AI agents.
- Design and ship evaluations, including extensions to the ComputeEval benchmark, to assess AI-powered CUDA development effectiveness.
- Fine-tune and specialize code LLMs, and collaborate on datasets and evaluations for NVIDIA's foundation models.
- Develop Agent Skills and MCP servers to integrate NVIDIA's developer tools (e.g., Nsight Compute, Nsight Systems) with AI agents.
- Generate, curate, and validate synthetic training and evaluation data for CUDA programming.
- Deliver new knowledge to LLMs through RAG and skill-based systems to keep models updated with NVIDIA's software stack.
- Collaborate with partner product teams to transition research prototypes into production features.
Requirements
- B.S. in Computer Science or related technical field, or equivalent experience (M.S. or Ph.D. is a plus).
- 12+ years of industry experience in applied AI/ML, with recent significant work in AI-for-code, coding agents, code LLMs, or AI developer tools.
- Strong proficiency in Python and solid software engineering practices.
- Hands-on experience fine-tuning or evaluating LLMs.
- Fluency with the systems aspects of LLM-powered agents, including context management, prompt caching, tool-use design, MCP, and Agent Skills.
- Experience designing or contributing to rigorous evaluations for code generation or agentic systems.
- Proven track record of shipping work beyond the prototype stage to real users.
- Comfortable working in a small, collaborative, in-person team with rapid changes and minimal process overhead.
Benefits
- Equity
- Benefits