Research Engineer / Research Scientist, RL Frontiers

Anthropic's mission is to create reliable, interpretable, and steerable AI systems.

The RL Scaling team works on how RL scales, focusing on throughput, stability, and learning efficiency as models grow larger and compute increases.

As a Research Engineer / Research Scientist, you will develop next-generation architectures and RL algorithms, scale them, and understand their behaviour.

Key responsibilities:

• Study how RL training and sampling scale with model size, context length, and compute
• Develop next-generation model architectures and RL algorithms
• Take small-scale results to frontier-scale runs and diagnose issues
• Build experimental infrastructure for research velocity
• Own end-to-end performance of large RL runs
• Build performance and cost models for proposed changes
• Investigate training dynamics at scale

Minimum qualifications:

• Familiarity with modern transformer language models
• Hands-on experience training large models in a distributed setting
• Track record of original technical work in ML training or systems
• Ability to design rigorous experiments at scale
• Quantitative reasoning about compute, memory, and communication costs
• Strong programming skills in Python and JAX or PyTorch

Preferred qualifications:

• Research experience in reinforcement learning, optimization, or large-scale training
• Experience developing RL algorithms for language models
• Experience with scaling laws or quantitative models of training efficiency
• Experience designing or modifying transformer architectures
• Experience scaling training to large fleets of accelerators
• Deep understanding of numerics in large-scale training
• Familiarity with GPU or TPU performance characteristics
• Experience with C++ or Rust

The annual compensation range for this role is $500,000-$850,000 USD.

Logistics:

• Minimum education: Bachelor’s degree or equivalent
• Required field of study: Relevant to the role
• Minimum years of experience: Correlates with internal job level requirements
• Location-based hybrid policy: 25% office time expected
• Visa sponsorship: Available