Applied Research Engineer, Chip Design

Job Title

Applied Research Engineer, Chip Design

Role Summary

Drive applied research that integrates large language models, agentic AI, and related ML techniques into NVIDIA's ASIC design flows to accelerate RTL generation, verification, PPA prediction/optimization, and validation. Work closely with chip design teams to move prototypes into production-quality tooling that shortens development schedules.

This is a research-engineering role focused on delivering measurable speed-ups for ASIC teams by embedding AI agents into EDA and validation flows.

Experience Level

Senior — requires substantial industry experience; posting requests 8+ years of proven industry experience.

Responsibilities

Primary responsibilities include applied research, prototyping, and delivery of AI-enabled tooling for ASIC design and validation.

• Apply LLMs, coding agents, and agentic systems to front-end ASIC problems: RTL generation, design & formal verification, timing, and PPA prediction/optimization.
• Develop, train, and evaluate models and agents (LLMs, RL, RLHF/RLAIF, post-training) using proprietary and synthetic technical data.
• Build robust data generation pipelines, evaluation methodologies, and graders to distinguish production-ready systems from demos.
• Integrate coding agents into EDA and validation flows: simulation, regressions, waveform/log analysis, and script generation.
• Iterate prototypes to production-quality tooling and measure impact against chip-design schedule metrics.
• Collaborate with model teams to improve domain-specific models and incorporate ASIC-design feedback into training and post-training workflows.

Requirements

Must-have technical skills and experience; items labeled "Nice-to-have" are optional.

• Must-have: 8+ years of proven industry experience delivering engineering or research outcomes in ASIC-related domains.
• Must-have: Domain expertise in front-end ASIC design, verification, and timing, with a track record of taking ideas from conception to production.
• Must-have: Hands-on experience building LLM-based agents or AI tooling used by real engineers, including context engineering, tool integration, orchestration, and failure analysis.
• Must-have: Experience with custom model training or fine-tuning (SFT, RLHF/DPO) on proprietary technical data and evaluation of model performance.
• Must-have: Experience building and maintaining infrastructure for ML and tooling (Docker, Slurm, CI/CD, etc.).
• Must-have: Strong collaboration and communication skills; ability to present complex technical work to engineers and stakeholders.
• Nice-to-have: Prior work integrating agents into EDA/validation pipelines or partnering with model teams to productionize domain-specific models.

Education Requirements

MS or PhD in Computer Science, Electrical/Computer Engineering, or a related field, or equivalent practical experience.

About the Company

Company: NVIDIA

Headquarters: Santa Clara, California, USA

NVIDIA is a global leader in accelerated computing, renowned for its innovative solutions in AI and digital twins that transform diverse industries. The company specializes in networking technologies, providing end-to-end InfiniBand and Ethernet solutions for servers and storage that optimize performance and scalability. NVIDIA serves sectors such as high-performance computing, enterprise data centers, and cloud computing, constantly reinventing its products and services to stay ahead in the market.