[Hiring] Applied Research - Chip Design @Prime Intellect

Role Description

Chip design is a natural fit for agentic reinforcement learning: long-horizon work, specialized tools, expensive mistakes, hard constraints, and objective feedback from simulation, verification, timing, power, and area. You will build agents, environments, evaluations, and post-training systems for real semiconductor design and verification workflows. The work is grounded in an external design workflow, with access to relevant domain experts and toolchains. It may span RTL generation and debugging, formal verification, simulation, physical design, optimization, and orchestration across EDA tools.

Responsibilities

• We turn a workflow into tasks, environments, evaluations, and reward signals, run post-training experiments against real baselines, and deploy what works.
• When a result looks good, we find out whether the gain came from the model or from the data, harness, inference, or infrastructure.
• The reusable parts become shared research, open-source systems, and product capabilities.
• Every Applied Research role is externally facing. Most researchers work directly with customers, users, or research partners while maintaining a substantive research agenda.

Representative early problems

• Build an interactive verification environment where an agent reads a specification, writes or repairs RTL, invokes simulation or formal tools, interprets failures, and iterates toward a verified result.
• Design rewards and evaluations that combine functional correctness with tool efficiency, robustness, and performance/power/area constraints without opening easy shortcuts.
• Train long-horizon agents to recover from design and tool failures, preserve useful state, and generalize across blocks, designs, and constraint sets.

What you will own

• 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 the 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 a willingness to change direction when the evidence changes.
• Strong Python and the ability to build artifacts that other researchers and engineers can run, inspect, and extend.
• Comfort owning ambiguous problems and keeping partners and teammates current as you resolve them.
• Evidence of exceptional work: publications, open-source systems, benchmarks, and shipped products.

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