Anthropic’s Research Engineer, Universes role combines research and engineering responsibilities to build the next generation of agentic training environments for safe and capable AI. The position focuses on reinforcement learning, environment design, rigorous evaluation, and collaboration across research and infrastructure teams. Candidates should have strong software engineering skills, research experience, and a passion for AI safety.
Research Engineer, Universes en Anthropic
Remoto - Remote-Friendly US
Más vacantes en AnthropicSalary
USD 500,000 - 850,000
Requirements
Skills
- Bachelor’s degree or an equivalent combination of education, training, and/or experience
- Highly impact-driven
- Operate with high agency
- Good research taste or senior technical experience, demonstrating good judgment in identifying what actually matters in complex problem spaces
- Balance research exploration with engineering implementation
- Passionate about the potential impact of AI and committed to developing safe and beneficial systems
- Comfortable with uncertainty and adapt quickly as the landscape shifts
- Strong software engineering skills and can build robust infrastructure
- Enjoy pair programming
- Industry experience with large language model training, fine-tuning or evaluation
- Industry experience building RL environments, simulation systems, or large-scale ML infrastructure
- Senior experience in a relevant technical field even if transitioning domains
- Deep expertise in sandboxing, containerization, VM infrastructure, or distributed systems
- Published influential work in relevant ML areas
Responsibilities
- Build the next generation of agentic environments
- Build rigorous evaluations that measure real capability
- Collaborate across research and infrastructure teams to ship environments into production training
- Debug and iterate rapidly across research and production ML stacks
- Contribute to research culture through technical discussions and collaborative problem-solving
Technologies
reinforcement learninglarge language model trainingfine-tuningevaluationsandboxingcontainerizationVM infrastructuredistributed systems
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