Applied Research, Chip Design

Prime Intellect

. Build agents, environments, evaluations, and post-training systems for semiconductor design and verification workflows .

Posted 10/19/2026full-timeRemote • California • United States, United KingdomMid-LevelSenior💰 $150,000 - $300,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Reinforcement Learning (RL), agent development, and post-training systems within semiconductor design workflows. Proficient in building interactive verification environments and conducting experiments to evaluate model performance and correctness.

Highest-signal resume keywords

Reinforcement LearningPython ProgrammingDesign AutomationModel TrainingExperimental Judgment

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Agent DevelopmentPost-Training SystemsInteractive Verification EnvironmentsEvaluation DesignSimulation ToolsFormal MethodsDebuggingArtifact CreationCredit AssignmentPerformance Evaluation

Soft Skills

Problem OwnershipCommunicationCollaborationAdaptabilityJudgment

Tools & Technologies

EDA ToolsSimulation ToolsFormal Verification Tools

Industry Keywords

Semiconductor DesignModel EvaluationFunctional CorrectnessTool EfficiencyRobustnessPower ConstraintsArea Constraints

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

Equity incentives
Flexible Work (San Francisco, New York, London, or remote)
Visa Sponsorship & relocation support
Professional Development budget
Team Off-sites & conference attendance