2 months ago
Base Salary
$500k - $850k/yr
Responsibilities
- Design and run experiments to improve Claude’s perception and agentic capabilities.
- Develop robust evaluation frameworks for measuring model performance on complex computer tasks.
- Build and improve computer-use and vision reinforcement-learning training environments.
- Create pipelines and tools to test and validate complex reinforcement-learning environments.
- Collaborate across the model training and infrastructure stack to improve production training.
- Partner with product teams to bring research advances into production.
Requirements
- Software engineering experience and proficiency in Python.
- Experience training, fine-tuning, or evaluating machine-learning models.
- Strong communication skills and a collaborative working style.
- Bachelor’s degree or an equivalent combination of education, training, and/or experience in a relevant field.
- Preferred experience training models for computer use or agentic capabilities.
- Preferred experience with reinforcement learning, especially long-horizon or sparse-reward settings.
- Preferred familiarity with multimodal model training.
- Preferred experience building evaluations or benchmarks for agentic systems.
- Preferred experience building reinforcement-learning environments, simulation systems, or large-scale ML infrastructure.
- Preferred experience collaborating with product teams to drive model improvements.
Benefits
- Hybrid policy requiring staff to work from an office at least 25% of the time, with some roles requiring more office time.
- Visa sponsorship is available, with immigration-lawyer support.
- Competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and office collaboration space.
Tech Stack
Categories
AI ResearchML Engineering
About Anthropic
We're an AI research company that builds reliable, interpretable, and steerable AI systems. Our first product is Claude, an AI assistant for tasks at any scale. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.