Machine Learning Engineer at Voltai (Palo Alto, CA)
Voltai· Palo Alto, CA·
Role details
Employment
Full-Time
Job description
About Voltai
Voltai is developing world models, and agents to learn, evaluate, plan, experiment, and interact with the physical world. We are starting out with understanding and building hardware; electronics systems and semiconductors where AI can design and create beyond human cognitive limits.
About the Team
Backed by Silicon Valley’s top investors, Stanford University, and CEOs/Presidents of Google, AMD, Broadcom, Marvell, etc. We are a team of previous Stanford professors, SAIL researchers, Olympiad medalists (IPhO, IOI, etc.), CTOs of Synopsys & GlobalFoundries, Head of Sales & CRO of Cadence, former US Secretary of Defense, National Security Advisor, and Senior Foreign-Policy Advisor to four US presidents.
What we're Looking For
- Strong AI/ML engineering skills from top tier CS, EECS, Math and Physics programs.
- Proven track record of delivering AI/ML projects from concept to production.
- Hands-on experience fine-tuning and deploying large language models (LLMs) in production environments.
- Prior experience working with multi-modal models (e.g., combining text, image, or audio inputs).
Bonus Points
- Background in competitive programming.
- Contributions to open-source initiatives.
- Notable awards or publications in leading journals/conferences.
- Experience thriving in a fast-paced, hyper-growth startup environment.
Why work at Voltai
- Culture: Emphasizes high-agency, interdisciplinary work at the intersection of AI and physical engineering. The team includes Olympiad medalists and former CTOs – a “smartest in the room” environment.
- Remote/hybrid policy: Fully remote (as indicated on Built In and LinkedIn). Employees work from the United States and Germany.
- Notable perks: Opportunity to work on frontier AI problems with direct impact on the semiconductor industry; access to unrivaled industry partners and compute resources.
- Engineering culture: Heavy focus on research engineering (CUDA, post-training, systems), with open roles for both software and hardware engineers. Emphasis on real-world validation and iterative experimentation.