Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems. Backed by Accel, Bessemer and General Catalyst.
About the role
Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons. The RL Scaling team works on how RL scales: what happens to throughput, stability, and learning efficiency as models get larger, episodes get longer, and compute grows by orders of magnitude, and what has to change in our algorithms and systems to keep getting returns from that scale. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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
More about this role
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. The RL Scaling team works on how RL scales: what happens to throughput, stability, and learning efficiency as models get larger, episodes get longer, and compute grows by orders of magnitude, and what has to change in our algorithms and systems to keep getting returns from that scale.
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...
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