Research Engineer, Machine Learning (RL Velocity)

Humanloop; Greater London; Hybrid; GBP 57,000 - 73,000; Full time

Benefits offered by this job

Visa sponsorship

Flexible working hours

Hybrid work policy

Hybrid office space

Job summary

Anthropic, a public benefit corporation, headquartered in San Francisco, focuses on reliable, interpretable, and steerable AI systems. Our RL Velocity team builds the infrastructure and tooling that help researchers iterate quickly and ship better models faster.

This role centers on improving RL training infrastructure, identifying bottlenecks, and collaborating with researchers and other engineering teams to deliver scalable, high-performance solutions.

Qualifications

• Bachelor’s degree or equivalent in a relevant field.
• Experience aligned with the internal job level.
• Strong software engineering fundamentals.
• Experience with ML infrastructure, distributed systems, or research tooling.
• Care about enabling others through platforms, not only experiments.
• Comfort across the stack from low-level performance to RL algorithms.
• Bias toward shipping and rapid iteration with high initiative.
• Experience with large-scale distributed training (RL, pre/post-training) is a plus.
• Familiarity with ML frameworks like JAX or PyTorch.
• Experience operating at the edge of research and infrastructure in a fast-moving environment.

Responsibilities

• Build and improve RL training infrastructure used by researchers daily.
• Identify and remove bottlenecks across the RL stack with profiling and rearchitecture.
• Partner with researchers and engineering teams to ship faster tooling.
• Own reliability and performance of research runs end-to-end.
• Contribute to design decisions for RL at scale.

Skills

Software engineering

ML infrastructure

RL tooling

Delivery mindset

High agency

Education

Bachelor's degree or equivalent

Tools

PyTorch

JAX

Job description

Salary: £57,000 - 73,000 per year

Requirements

• We expect a Bachelors degree or an equivalent combination of education, training, and/or experience.
• We look for a field relevant to the role, demonstrated through coursework, training, or professional experience.
• We expect years of experience to align with the internal job level requirements for the position.
• You should have strong software engineering fundamentals and a track record of building performant, reliable systems.
• You should have experience with ML infrastructure, distributed systems, or research tooling.
• You should care about enabling other peoples work and find leverage through platforms rather than individual experiments.
• You should be comfortable operating across the stack, from low-level performance work to RL algorithms.
• You should have a bias toward shipping and iterating quickly, with high agency and low ego.
• Strong candidates may also have experience with large-scale distributed training, such as RL, pre-training, or post-training.
• Strong candidates may also have familiarity with JAX, PyTorch, or similar ML frameworks.
• Strong candidates may also have a track record of operating at the edge of research and infrastructure in a fast-moving environment.

Responsibilities

• We build and improve the RL training infrastructure that researchers depend on day to day.
• We identify and remove bottlenecks across the RL stack, including debugging, profiling, and rearchitecting where needed.
• We partner closely with researchers and adjacent engineering teams, including inference, sandboxing, and others, to understand pain points and ship tooling that makes them faster.
• We own the reliability and performance of research runs end to end.
• We contribute to design decisions that shape how we do RL at scale.

Technologies

• AI
• PyTorch

More

We are Anthropic, a public benefit corporation headquartered in San Francisco, and our mission is to create reliable, interpretable, and steerable AI systems that are safe and beneficial for our users and for society. Our team is a fast-growing group of researchers, engineers, policy experts, and business leaders working together on high-impact AI research. The RL Velocity team focuses on the efficiency and reliability of our RL Science stack, building the infrastructure and tooling that help researchers iterate quickly and ship better models faster. We offer competitive compensation, optional equity donation matching, generous vacation and parental leave, flexible working hours, a lovely office space to collaborate, and a location-based hybrid policy that currently expects staff in one of our offices at least 25% of the time. We also sponsor visas where possible and encourage candidates to apply even if they do not meet every qualification.

last updated 36 week of 2026