Our client is a startup building AI agents and a code editor workflow to revolutionize semiconductor design, an industry grappling with skyrocketing costs and slow timelines. By making silicon engineers up to 4x more productive, their platform addresses critical bottlenecks in hardware architectural exploration, SystemVerilog construction, and physical design. The company, founded in 2025, is a small team of six with a mission to cut chip design from three years to just six months. They are backed by top advisors, including Stanford professors and DeepMind Chief Scientists, and have a high-profile investor in Jeff Dean.
Our client is seeking a Founding Machine Learning Architect to define and lead their AI roadmap. This is a foundational hire who will report directly to the Head of ML and work closely with the founders. You will have a rare opportunity to architect and deploy agent-based systems from the ground up, impacting a massive industry. This is a hands-on role for an individual who is excited to go from 0 to 1 and shape the core ML architecture. The hiring window for this critical role closes on September 15th.

You will be responsible for:

• Building the ML roadmap and research strategy for RTL and physical design domains.
• Architecting and deploying agent-based systems for RTL code generation and physical design automation.
• Leading, mentoring, and scaling a top-tier ML engineering team.
• Collaborating with hardware engineers to identify and solve pipeline bottlenecks with ML.

REQUIREMENTS

We are seeking a deeply technical, hands-on ML Architect with a strong academic and practical background in AI. The ideal candidate has the ability to synthesize complex research and translate it into a production-ready system.

CORE REQUIREMENTS:

• Deep expertise in LLMs and Reinforcement Learning.
• A PhD is strongly preferred, as it indicates a strong research depth (5+ years of research vs. 8 months for a Master’s).
• Exceptional ability to read and synthesize research papers.
• 5+ years of experience in ML at a top-tier AI lab or company.
• Proven leadership in high-performance engineering teams.

• Building the ML roadmap and research strategy for RTL and physical design domains.
• Architecting and deploying agent-based systems for RTL code generation and physical design automation.
• Leading, mentoring, and scaling a top-tier ML engineering team.
• Collaborating with hardware engineers to identify and solve pipeline bottlenecks with ML