Applied Research Engineer, Chip Design
$192k - $357k • US, CA, Santa Clara
Posted 1d ago
Job Location
US, CA, Santa Clara
Tech Stack
Remote Work Policy
On-site
Categories
AI Engineer
About the job
NVIDIA is seeking a world-class engineer to drive applied research at the intersection of AI and ASIC design. This role is at the forefront of transforming chip design using large language models, coding agents, and agentic AI, applying the latest AI advancements to NVIDIA's real ASIC design flows. The ideal candidate is a creative, collaborative researcher who wants their work to result in tangible silicon products on a real schedule, contributing to NVIDIA's legacy of innovation.
Responsibilities
- Apply LLMs, coding agents, and agentic systems to core ASIC design problems such as RTL generation, design and formal verification, and PPA prediction and optimization.
- Deliver against NVIDIA's internal chip design schedules, with success measured by the acceleration of ASIC team progress.
- Build robust data generation pipelines, including synthetic data, and meticulous evaluation methodologies to identify effective systems.
- Integrate coding agents and agentic AI into EDA and validation flows, including simulation, regressions, waveform and log analysis, and script generation.
- Advance chip design capabilities by developing models and research harnesses on open-source foundations, iterating rapidly from prototype to production.
- Collaborate with NVIDIA's internal Nemotron team to enhance models using domain-specific data and feedback, and integrate ASIC design expertise back into the models.
Requirements
- MS or PhD or equivalent experience in Computer Science, Electrical/Computer Engineering, or a related field.
- 8+ years of proven industry experience.
- Domain and technical expertise in front-end ASIC (design, verification, timing).
- Project experience applying agentic AI to chip design and optimization problems, with a track record of end-to-end project delivery.
- Hands-on experience building LLM-based agents or AI tooling, focusing on context engineering, tool integration, orchestration, and failure analysis.
- Experience with custom model training, fine-tuning, or post-training (SFT, RLHF/DPO) over proprietary technical data.
- Experience building and maintaining infrastructure (Docker, Slurm, CI/CD).
- Excellent self-motivation, creativity, collaboration skills, and the ability to work effectively within a team.
- Excellent written and verbal communication skills, with proven experience presenting complex technical work.
Benefits
- Equity
- Benefits