Research Engineer / Research Scientist, RL Frontiers

San Francisco, CA | New York City, NY | Seattle, WA · San Francisco · USD 500,000-850,000 / year · Hybrid · Posted Sep 29, 2026

At a glance

Workplace: Hybrid
Location: San Francisco, CA | New York City, NY | Seattle, WA · San Francisco
Salary: USD 500,000-850,000 / year · Base salary
Visa sponsorship: Visa sponsorship available

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons.
This role sits squarely across research and engineering, requiring the development of next-generation architectures and RL algorithms to run at frontier scale.

Study how RL training and sampling scale with model size, context length, and compute, and find the algorithmic and systems changes that keep scaling efficient.
Develop next-generation model architectures and RL algorithms, and make them run efficiently at frontier scale.
Take promising small-scale results to frontier-scale runs, and diagnose why they behave differently when they get there, whether the cause is numerical, algorithmic, or systemic.
Build the experimental infrastructure that sets research velocity: fast, reproducible comparisons of architecture and algorithm variants at meaningful scale.
Own end-to-end performance of our largest RL runs, from research code down to the hardware.
Build performance and cost models for proposed architecture and algorithm changes, and use them to decide which ideas get scaled.
Investigate training dynamics at scale, including instabilities, divergence, and throughput regressions, and trace them to root cause.

Deep familiarity with modern transformer language models, including their architecture, training dynamics, and the behavior of large-scale optimization.
Hands-on experience training large models in a distributed setting, including the tradeoffs between data, tensor, and pipeline parallelism.
A track record of original technical work in ML training or systems, such as new methods, architectures, or optimizations, demonstrated through research, open-source, or production impact.
Ability to design rigorous experiments at scale, including baselines, ablations, and enough statistical care to trust a result that costs real compute.
Ability to reason quantitatively about the compute, memory, and communication costs of a model or algorithm.
Strong programming skills in Python and JAX or PyTorch, and comfort reading and changing code at every layer of the stack.
Bachelor’s degree or an equivalent combination of education, training, and/or experience in a relevant field.

Research experience in reinforcement learning, optimization, or large-scale training, published or otherwise
Experience developing RL algorithms for language models
Experience with scaling laws or other quantitative models of training efficiency
Experience designing or modifying transformer architectures beyond standard configurations
Experience scaling training to large fleets of accelerators and debugging the problems that only appear at scale
Deep understanding of numerics in large-scale training, including low-precision formats and sources of instability
Familiarity with how GPU or TPU performance characteristics shape architecture and algorithm choices
Experience with C++ or Rust

Location-based hybrid policy: currently expect all staff to be in one of the offices at least 25% of the time, though some roles may require more time in offices.