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 Data & Evaluation Engineer on the Horizons team, you will build the software infrastructure that enables our AI models to use tools effectively and measure their performance. You'll develop and extend our agent framework, create and implement evaluations, manage training data pipelines, and apply data science techniques to improve model capabilities. This engineering-focused role combines software development with empirical analysis to drive advances in model performance and capabilities.
Representative projects:
- Extend and improve our agent framework that enables models to interact with tools and environments
- Design and implement evaluation systems that rigorously measure model capabilities across tasks
- Build and maintain data pipelines for collecting, processing, and managing RL training data
- Develop dashboards and analysis tools to extract insights from model performance data
- Collaborate with researchers to translate evaluation needs into scalable, production-grade systems
You may be a good fit if you:
- Are proficient in Python and data analysis libraries (Pandas, NumPy, etc.)
- Have experience creating data visualizations and interactive dashboards
- Enjoy translating complex research questions into concrete measurement approaches
- Possess strong software engineering fundamentals with an emphasis on clean APIs
- Can communicate technical concepts clearly to both engineers and researchers
- Have experience with web development for interactive tools (JavaScript, React, etc.)
- Are passionate about measuring and improving AI capabilities and safety
Strong candidates may have:
- Experience with LLM specific evaluations and frameworks
- Experience with data visualization libraries and frameworks (D3.js, Plotly, Grafana)
- Familiarity with the Jupyter ecosystem and notebook-based workflows
- Knowledge of statistical methods and experimental design
- Experience with web frameworks for building interactive applications (FastAPI, Flask)
- Experience working with large datasets and understand performance considerations
- Comfort engaging with ML research papers and implementing metrics from academic literature
Strong candidates need not have:
- Formal certifications or education credentials
- Prior experience with reinforcement learning research
Deadline to apply: None. Applications will be reviewed on a rolling basis.
