Research Engineer - Post-Training

About the role

Post-train frontier models to autonomously perform complex tasks across the semiconductor design and verification pipeline. Models you train will propose and optimize chip architectures, generate and refine RTL code, run simulations, identify verification gaps, and iteratively improve designs—accelerating the pace of semiconductor innovation.

Responsibilities

• Create and scale reinforcement learning (RL) environments for large language models (LLMs) or multimodal agents.
• Build high-quality evaluation datasets and benchmarks for complex reasoning or design tasks.
• Collaborate with leading experts in hardware design, verification, and computer architecture to design rich RL environments that capture the intricacies of chip design workflows.
• Develop structured reward functions, scaling strategies, and evaluation frameworks that push models toward higher reliability, efficiency, and creativity in semiconductor reasoning.
• Apply reinforcement learning or curriculum learning to structured reasoning or symbolic domains.

Requirements

Experience with:

• Creating and scaling RL environments for LLMs or multimodal agents.
• Building high-quality evaluation datasets and benchmarks for complex reasoning or design tasks.
• Collaborating with domain experts in hardware and verification to define evaluation metrics, constraints, and simulation conditions.
• Designing reward functions and feedback pipelines that balance correctness, performance, and design efficiency.
• Running large-scale RL fine-tuning or post-training experiments for frontier models.
• Applying reinforcement learning or curriculum learning to structured reasoning or symbolic domains.

Qualifications

Preferred qualifications include:

• Ph.D. in Computer Science, Electrical Engineering, or related field.
• Experience with reinforcement learning, deep learning, and/or computer vision.
• Strong background in hardware design, verification, and computer architecture.
• Ability to work effectively with cross-functional teams and domain experts.
• Excellent communication and collaboration skills.

Skills

Required skills include:

• Reinforcement Learning
• Deep Learning
• Computer Vision
• Hardware Design
• Verification
• Computer Architecture

Benefits

Our benefits package includes:

• Competitive compensation
• Flexible working hours
• Professional development opportunities
• Health insurance
• Retirement savings plans

Pay

Salary range: $150,000 - $200,000 per year

Schedule

Full-time position