Research Engineer, Chip Design RL

Job Title

Research Engineer, Chip Design RL

Role Summary

The Code RL team at Anthropic applies reinforcement learning to improve model capabilities for hardware and silicon design. This role develops RL environments and tooling for RTL generation, verification, and physical design, and moves research prototypes into production training runs.

Experience Level

Mid-level. The posting does not specify years of experience.

Responsibilities

Key responsibilities include:

• Design and implement RL environments for RTL generation and verification.
• Optimize physical design flows and perform formal verification.
• Reduce EDA-tool latency and design proxy rewards for ML training.
• Plan and run experiments that shape the research roadmap.
• Deliver research成果 into research and production training runs.
• Collaborate with researchers and engineers across Anthropic.

Requirements

Must-have technical skills and experience.

• Expertise in ASIC or FPGA design, including RTL development and design verification.
• Experience with physical design processes such as synthesis and timing closure.
• Fluency with industry EDA tools and standard chip-design processes.
• Practical experience taking chip designs from specification toward silicon.
• Familiarity with reinforcement learning and applying it to engineering problems.
• Ability to balance research exploration with engineering implementation and delivery.
• Nice-to-have: experience with ML accelerators / high-performance compute hardware and familiarity with high-level synthesis or architecture simulators.

Education Requirements

Bachelor's degree or equivalent in a relevant field, or equivalent practical experience (source states "Bachelor’s degree or equivalent in a relevant field").

About the Company

Company: Anthropic

Headquarters: San Francisco, CA, United States

Anthropic is an AI research and safety company developing reliable, interpretable, and steerable large language models (Claude) and related tools, focusing on alignment, reinforcement learning, and scalable infrastructure for safe AI deployment.