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 an Infrastructure & Runtime Engineer on the Horizons team, you will build and maintain the foundational systems that enable our AI research. You'll work closely with researchers and engineers to develop robust infrastructure for large language model training, focusing on code execution environments, data pipelines, and performance optimization. Your work will directly support advances in reinforcement learning, agentic AI capabilities, and secure model evaluation systems.
Representative projects:
- Design and implement high-performance data pipelines for processing large-scale code datasets with an emphasis on reliability and reproducibility
- Build and maintain secure sandboxed execution environments using virtualization technologies like GVisor and Firecracker
- Develop infrastructure for reinforcement learning training environments, balancing security requirements with performance needs
- Optimize resource utilization across our distributed computing infrastructure through profiling, benchmarking, and systems-level improvements
- Collaborate with researchers to translate their requirements into scalable, production-grade systems for AI experimentation
You may be a good fit if you:
- Are proficient in Python and async/concurrent programming with frameworks like Trio
- Have experience with container technologies and virtualization systems
- Possess strong systems programming skills and understand performance optimization
- Enjoy solving complex infrastructure challenges at scale
- Have experience with data pipeline development and ETL processes
- Care deeply about code quality, testing, and performance
- Communicate effectively with both technical and research-focused team members
- Are passionate about developing safe and beneficial AI systems
Strong candidates may have:
- Experience with cloud infrastructure and Kubernetes orchestration
- Familiarity with infrastructure-as-code tools (Terraform, Pulumi, etc.)
- Experience contributing to open-source projects in systems or infrastructure
- Knowledge of Rust and/or C++ for performance-critical components
- Experience implementing security controls for code execution
- Comfort engaging with ML research concepts and translating them to engineering requirements
Strong candidates need not have:
- Formal certifications or education credentials
- Experience with LLMs, reinforcement learning, or machine learning research before
Deadline to apply: None. Applications will be reviewed on a rolling basis.
The expected salary range for this position is:
Annual Salary:
$315,000—$425,000 USD
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. 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. 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.