Research Engineer / Research Scientist, RL Frontiers

About the Position

We are looking for a Research Engineer / Research Scientist, RL Frontiers to join our team at Anthropic. 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.

About Anthropic

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.

Key Responsibilities

• 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

Minimum Qualifications

• 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

Preferred Qualifications

• 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

We Offer

• Competitive salary range: $500,000 - $850,000 USD annually
• Hybrid work location policy: Currently, we expect all staff to be in one of our offices at least 25% of the time
• Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

Locations

• San Francisco, CA
• New York City, NY
• Seattle, WA

Apply Now

Interested candidates should apply with their resume and any relevant publications or GitHub repositories. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

How We're Different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact - advancing our long-term goals of steerable, trustworthy AI - rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.