Research Engineer / Research Scientist, RL Frontiers

The shape of it

In the posting’s own words

This role sits squarely across research and engineering. You'll develop next-generation architectures and RL algorithms, take them from a small-scale result to a frontier-scale run, and understand every place they behave differently along the way. You'll build the systems that set how fast the team can iterate: how many experiments, at what scale, and how quickly we can trust the results. And you'll work on Anthropic's largest and fastest RL runs, where the gap between a good idea and a working one is often a problem no one has solved yet.

What it asks for · 6

• 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

Also a plus

• 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

What the job covers

• 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

Tools and skills named

Words the posting leans on

• models12×
• scale12×
• algorithm10×
• architecture9×
• training9×
• experience7×
• run6×
• change5×
• compute5×
• get5×
• research5×
• systems5×
• throughput5×
• build4×
• cost4×
• learning4×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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