Research Engineer, RL Engineering

Anthropic; Seattle (WA); Hybrid; USD 520,000 - 850,000; Full time

Benefits offered by this job

Equity donation matching

Generous vacation

Parental leave

Flexible working hours

Office space

Job summary

Anthropic is seeking an ML Systems Engineer on the Reinforcement Learning Engineering team to build the algorithms and infrastructure used to train Claude-like models. You will focus on performance, robustness, and usability to accelerate research progress and model capabilities.

You will work on production RLHF training systems, improve tooling, and collaborate with researchers. This role blends research support with engineering ownership, in a hybrid office setting.

Qualifications

• 4+ years of software engineering experience.
• Like working on systems and tools that make other people more productive.
• Are results-oriented, with a bias towards flexibility and impact.
• Pair programming is valued and practiced.
• Interest in machine learning research.
• Care about the societal impacts of your work.

Responsibilities

• Build, maintain, and improve algorithms and systems that researchers use to train models.
• Improve speed, reliability, and ease-of-use of these systems.
• Support and empower our research team to progress quickly and safely.
• Profiling and optimizing RL training pipelines and infrastructure.

Skills

4+ years software engineering

Pair programming

Results oriented

Interest in ML research

Societal impact

Productivity tooling

Education

Bachelor’s degree or equivalent

Tools

Python

Job description

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.

About The Role

You want to build the cutting-edge systems that train AI models like Claude. You're excited to work at the frontier of machine learning, implementing and improving advanced techniques to create ever more capable, reliable and steerable AI. As an ML Systems Engineer on our Reinforcement Learning Engineering team, you'll be responsible for the critical algorithms and infrastructure that our researchers depend on to train models. Your work will directly enable breakthroughs in AI capabilities and safety. You'll focus obsessively on improving the performance, robustness, and usability of these systems so our research can progress as quickly as possible. You're energized by the challenge of supporting and empowering our research team in the mission to build beneficial AI systems. Our finetuning researchers train our production Claude models, and internal research models, using RLHF and other related methods. Your job will be to build, maintain, and improve the algorithms and systems that these researchers use to train models. You'll be responsible for improving the speed, reliability, and ease-of-use of these systems.

You May Be a Good Fit If You

• Have 4+ years of software engineering experience
• Like working on systems and tools that make other people more productive
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Enjoy pair programming (we love to pair!)
• Want to learn more about machine learning research
• Care about the societal impacts of your work

Strong Candidates May Also Have Experience With

• High performance, large scale distributed systems
• Large scale LLM training
• Python
• Implementing LLM finetuning algorithms, such as RLHF

Representative Projects

• Profiling our reinforcement learning pipeline to find opportunities for improvement
• Building a system that regularly launches training jobs in a test environment so that we can quickly detect problems in the training pipeline
• Making changes to our finetuning systems so they work on new model architectures
• Building instrumentation to detect and eliminate Python GIL contention in our training code
• Diagnosing why training runs have started slowing down after some number of steps, and fixing it
• Implementing a stable, fast version of a new training algorithm proposed by a researcher

Deadline to apply

None. Applications will be reviewed on a rolling basis.

An 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.

Annual Salary

$500,000-$850,000 USD

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.

Benefits

• 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.

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.

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.

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.