Research Engineer, RL Scaling Science

Mat Vin; Greater London; Hybrid; GBP 106,000 - 144,000; Full time

Benefits offered by this job

Competitive compensation

Equity option

Generous vacation and parental leave

Flexible working hours

Office space in SF

Job summary

Anthropic is seeking a Research Engineer for the RL Scaling Science team to design and run large-scale reinforcement learning experiments and ship validated findings into production training. This role sits at the research/engineering boundary, requiring rigorous experimentation, benchmarks, and collaboration with adjacent RL teams to advance our scalable AI systems.

You will work with Python on frontier-scale training, translating insights into practical training recipes while considering

Qualifications

• Strong empirical research skills in Reinforcement Learning or related ML areas.
• Experience designing and running large-scale experiments.
• Ability to translate research findings into production training recipes.
• Proven capability to own end-to-end experiments from design to interpretation.

Responsibilities

• Design, run, and interpret large-scale RL experiments, with rigorous analysis.
• Investigate RL behavior as horizon, compute, and model size increase.
• Build benchmarks for long-horizon RL to ensure measurable progress.
• Translate validated findings into production training recipes.
• Debug complex issues at the research/systems boundary and scale.
• Collaborate with adjacent RL teams to advance the RL stack.

Skills

Python

Large-scale ML training

Reinforcement Learning

Experiment design

Productionization

Education

Bachelor’s degree or equivalent (relevant field)

Tools

Distributed ML systems

Job description

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.

About the role

Anthropic's RL Scaling Science team studies how reinforcement learning behaves as we scale it (across model size, compute, and task horizon) and turns that understanding into the training recipes behind our frontier models. As a Research Engineer on this team, you'll design and run large-scale experiments to understand and resolve bottlenecks, build the benchmarks that make long-horizon progress measurable, and ship validated findings directly into production training.

This role lives at the boundary between research and engineering. The problems are open, the experiments run at frontier scale, and the path from a robust result to production is short.

Key responsibilities

• Design, run, and interpret large-scale RL experiments, reasoning rigorously about what the data does and doesn't show
• Investigate how RL improves as horizon, compute, and model size grow
• Build and maintain benchmarks for long-horizon RL so progress is measurable and reproducible
• Translate validated findings into production training recipes, exercising judgment about when a result is robust enough to ship
• Debug complex issues at the seam where research meets infrastructure - failures that only appear at scale
• Partner closely with adjacent RL teams across research and engineering and advance our overall RL stack
• Strong empirical research skills in Reinforcement Learning, large-scale ML training, or a closely adjacent area
• Demonstrated ability to own large experiments end-to-end, from design through interpretation
• Proficiency in Python and experience working with large-scale or distributed ML systems
• Comfort operating at the research/systems boundary, including debugging where the two meet
• Care about the societal impacts of AI and responsible scaling

Preferred qualifications

• Published or shipped work in long-horizon RL or RL fundamentals
• Experience translating research findings into production training recipes
• Demonstrated large scale industry impact via RL interventions
• Experience working on frontier-scale training runs with long trajectories

Representative projects

• Design a benchmark suite for long-horizon RL that distinguishes genuine capability gains from artifacts of evaluation setup
• Take a promising experimental finding, stress-test it across model scales, and work with training teams to land it in a production recipe
• Investigate an unexpected scaling trend in an RL run and trace it to a root cause spanning algorithm, data, and infrastructure

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Logistics

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

Required field of study:A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

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

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.

We encourage you to apply even if you do not believe you meet every single qualification. 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. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you [e-mail] addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

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.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage:Learn about our policy for using AI in our application process.