Research Engineer, Machine Learning (Horizons)

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 Horizons The Horizons team leads Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude 3.5 and 3.7 Sonnet. Our work spans several key areas :

Developing systems that enable models to use computers effectively

Advancing code generation through reinforcement learning

Pioneering fundamental RL research for large language models

Building scalable RL infrastructure and training methodologies

Enhancing model reasoning capabilities

We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and work hand-in-hand with dedicated RL engineering teams to implement our research at scale. The Horizons team sits at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish.

About the Role As a Research Engineer on the Horizons team, 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. You\'ll work on fundamental research in reinforcement learning, creating \'agentic\' models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation.

Representative projects 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.

You may be a good fit if you Are proficient in Python and async / concurrent programming with frameworks like Trio

Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX)

Have industry experience in machine learning research

Can balance research exploration with engineering implementation

Enjoy pair programming (we love to pair!)

Care about code quality, testing, and performance

Have strong systems design and communication skills

Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems

Strong candidates may have 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++

Strong candidates need not have Formal certifications or education credentials

Academic research experience or publication history

Deadline to apply : None. Applications will be reviewed on a rolling basis.

Logistics Education requirements : We require at least a Bachelor\'s degree in a related field or equivalent experience.

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. 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. 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. 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.

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.

J-18808-Ljbffr