Applied Research - Chip Design
Prime Intellect· Applied Research
San Francisco, New York, Remote, London$150k - $300kFull-timePosted 5d ago
About the role
Prime Intellect is building the open superintelligence stack and seeks researchers to design and validate agentic systems for real semiconductor design and verification workflows, spanning RTL, formal verification, simulation, physical design, and tooling orchestration.
Responsibilities
- Interactive environments where agents use EDA tools to modify designs, debug failures, and plan multi-step work.
- Verifiers and rewards grounded in tests, simulation, formal methods, design rules, and engineering outcomes.
- Experiments on credit assignment when feedback arrives late and several tools contribute.
- Evaluations for correctness, recovery, and generalization, with reusable methods for other simulator-grounded engineering domains.
Qualifications
- Depth in RL, agents, or post-training systems and fluency in semiconductor workflows.
- Prior work in design automation, compilers, formal methods, or ML for systems useful but not required.
- Hands-on experience building and debugging model-training, post-training, agent, evaluation, or closely adjacent experimental loops.
- Strong experimental judgment, meaningful baselines, careful trace inspection, clear failure attribution, and adaptability.
- Strong Python and ability to build artifacts that others can run, inspect, and extend.
- Comfort owning ambiguous problems and keeping partners and teammates current as you resolve them.
Skills
Required:Reinforcement Learning (RL)Post-training systemsPython
Benefits
- Cash Compensation Range of $150-300k + equity incentives
- Flexible Work (San Francisco, New York, London, or remote)
- Visa Sponsorship & relocation support
- Professional Development budget
- Team Off-sites & conference attendance
Last updated Oct 21, 2026. Interested? Apply on the company site.
About Prime Intellect
Commoditizing Compute & Intelligence
Prime Intellect democratizes AI development at scale. Our platform makes it easy to find global compute resources and train state-of-the-art models through distributed training across clusters. Collectively own the resulting open AI innovations, from language models to scientific breakthroughs.
AI developmentbessemer-ai-infrastructure-2026compute resourcesdistributed trainingmodelsreinforcement-learning-platformsscience
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