Founding Machine Learning Architect
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.
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.
Must-Haves:
- Strong proficiency in Python, PyTorch, and RL libraries.
- Experience with Transformers and MLOps.
- A strong track record of publications or product development.
- A startup builder mindset and a passion for working in an early-stage environment.
- Willingness to work in-person, 5 days a week, in Palo Alto, CA.
Bonus Points:
- Prior experience with AI for chip design.
- Experience at a top-tier research lab (e.g., Google, NVIDIA, OpenAI, DeepMind).
- Salary: $300,000–$400,000
- Equity: Competitive
- Visa Sponsorship: O1 and H1B sponsorships are available
Due to the high volume of applications we anticipate, we regret that we are unable to provide individual feedback to all candidates. If you do not hear back from us within 4 weeks of your application, please assume that you have not been successful on this occasion. We genuinely appreciate your interest and wish you the best in your job search.
Commitment to Equality and Accessibility:
At MLabs, we are committed to offer equal opportunities to all candidates. We ensure no discrimination, accessible job adverts, and providing information in accessible formats. Our goal is to foster a diverse, inclusive workplace with equal opportunities for all. If you need any reasonable adjustments during any part of the hiring process or you would like to see the job-advert in an accessible format please let us know at the earliest opportunity by emailing [email protected].
MLabs Ltd collects and processes the personal information you provide such as your contact details, work history, resume, and other relevant data for recruitment purposes only. This information is managed securely in accordance with MLabs Ltd’s Privacy Policy and Information Security Policy, and in compliance with applicable data protection laws. Your data may be shared only with clients and trusted partners where necessary for recruitment purposes. You may request the deletion of your data or withdraw your consent at any time by contacting [email protected].
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