Applied Research, Chip Design
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Job Description
Applied Researcher building RL agents, EDA environments, and evaluations for semiconductor design and verification. Prime Intellect develops open infrastructure and models for frontier AI teams.
Responsibilities:
- Build agents, environments, evaluations, and post-training systems for semiconductor design and verification workflows
- Turn workflows into tasks, environments, evaluations, and reward signals
- Run post-training experiments against real baselines
- Determine whether gains come from the model, data, harness, inference, or infrastructure
- Build interactive verification environments where agents read specifications, write or repair RTL, invoke simulation or formal tools, interpret failures, and iterate toward verified results
- Design rewards and evaluations combining functional correctness, tool efficiency, robustness, and performance/power/area constraints
- Train long-horizon agents to recover from design and tool failures, preserve useful state, and generalize across blocks, designs, and constraint sets
- Develop interactive environments where agents use EDA tools to modify designs, debug failures, and plan multi-step work
- Develop verifiers and rewards grounded in tests, simulation, formal methods, design rules, and engineering outcomes
- Experiment on credit assignment when feedback arrives late and several tools contribute
- Create evaluations for correctness, recovery, and generalization
- Work directly with customers, users, or research partners while maintaining a substantive research agenda
Requirements:
- Depth in RL, agents, or post-training systems and ability to become fluent in semiconductor workflows
- Prior work in design automation, compilers, formal methods, or ML for systems is 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 willingness to change direction when evidence changes
- Strong Python
- Ability to build artifacts that other researchers and engineers can run, inspect, and extend
- Comfort owning ambiguous problems and keeping partners and teammates current
- Evidence of exceptional work such as publications, open-source systems, benchmarks, or shipped products
Benefits:
- Equity incentives
- Flexible Work (San Francisco, New York, London, or remote)
- Visa Sponsorship & relocation support
- Professional Development budget
- Team Off-sites & conference attendance
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