Research Engineer, Machine Learning (Reinforcement Learning)

About the role

As a Research Engineer within Reinforcement Learning, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to the research direction.

Responsibilities

• Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters.
• Help scale our systems to handle increasingly complex research workflows.
• Design, implement, and test novel training environments, evaluations, and methodologies for reinforcement learning agents which push the state of the art for the next generation of models.
• Drive performance improvements across our stack through profiling, optimization, and benchmarking.
• Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation workflows.
• Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research.

Requirements

• Proficient in Python and async/concurrent programming with frameworks like Trio.
• Experience with machine learning frameworks (PyTorch, TensorFlow, JAX).
• Industry experience in machine learning research.
• Balance research exploration with engineering implementation.
• Enjoy pair programming (we love to pair!)
• Strong systems design and communication skills.
• Pasion for the potential impact of AI and commitment to developing safe and beneficial systems.

Qualifications

• Familiarity with LLM architectures and training methodologies.
• Experience with reinforcement learning techniques and environments.
• Experience with virtualization and sandboxed code execution environments.
• Experience with Kubernetes.
• Experience with distributed systems or high-performance computing.
• Experience with Rust and/or C++.

Skills

• Python and async/concurrent programming.
• Machine learning frameworks (PyTorch, TensorFlow, JAX).
• Reinforcement learning techniques and environments.
• Kubernetes.
• Distributed systems or high-performance computing.
• Rust and/or C++.

Benefits

• Competitive compensation and benefits.
• Optional equity donation matching.
• Generous vacation and parental leave.
• Flexible working hours.
• Lovely office space for collaboration.

Pay

The annual compensation range for this role is $500,000—$850,000 USD.

Schedule

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.

Location-based Hybrid Policy

We currently 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.

Equal Opportunity Employer

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. 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.

Guidance on Candidates' AI Usage

Learn about our policy for using AI in our application process.