Research Scientist

ChipAgents•San Jose, CA

4d•$150,000 - $350,000•Onsite

About The Position

ChipAgents is redefining the future of chip design and verification with agentic AI workflows. Our platform leverages cutting-edge generative AI to assist engineers in RTL design, simulation, and verification, dramatically accelerating chip development. Founded by experts in AI and semiconductor engineering, we partner with top semiconductor firms, cloud providers, and innovative startups to build intelligent AI agents. The company is a Series A company backed by tier-1 VC firms. ChipAgents is deployed in production to companies that have shipped 16B chips. We are seeking Research Scientists with deep expertise in AI and machine learning to pioneer the next generation of AI-assisted electronic design automation. In this role, you'll conduct research that directly impacts chip designers, working at the intersection of cutting-edge AI and real-world semiconductor workflows. You'll train and deploy large language models at scale, architect intelligent agent systems, and transform research breakthroughs into production-ready tools used by leading hardware companies.

Requirements

Degree in Computer Science, Machine Learning, Electrical Engineering, or a related field (or equivalent research experience).
Strong background in large language models, deep learning, and modern ML frameworks (e.g., PyTorch, JAX).
Experience with AI agent architectures, autonomous systems, or multi-agent workflows.
Strong programming skills in Python and experience with large-scale model training infrastructure.
Ability to balance research rigor with pragmatic engineering to deliver real-world impact.

Nice To Haves

Familiarity with electronic design automation (EDA) tools, RTL design, or hardware-software co-design is a strong plus.

Responsibilities

Conduct research in AI-assisted EDA, focusing on practical solutions that accelerate chip design and verification workflows.
Train and deploy large language models using substantial compute resources, optimizing for both performance and real-world applicability.
Design, build, and benchmark agentic AI systems using state-of-the-art LLMs and proprietary in-house models.
Translate research innovations into production-ready features that integrate seamlessly into hardware engineering workflows.
Collaborate with cross-functional teams to identify high-impact research directions based on customer needs and field deployment learnings.
Publish findings and contribute to the broader AI and EDA research communities where appropriate.