Anthropic seeks an exceptional Research Engineer for its Life Sciences team to accelerate progress in biology through cutting‑edge AI. The role blends deep expertise in machine learning engineering with rigorous evaluation and training of large language models, collaborating with top researchers and engineers to build safe, beneficial AI systems.
Research Engineer, Life Sciences at Anthropic
Hybrid - San Francisco, CA
More jobs at AnthropicSalary
USD 350,000 - 500,000
Requirements
Skills
- Demonstrated experience training and evaluating large language models
- Proficiency in Python and familiarity with modern ML development practices
- Experience building and managing data pipelines for large-scale datasets
- Comfortable navigating ambiguity and developing solutions in rapidly evolving research environments
- Strong written and verbal communication skills, with the ability to work independently while collaborating effectively across cross‑functional teams
- 8+ years of machine learning experience
- Prior work experience in AI and biology, including graduate studies (molecular biology, biochemistry, computational biology, or related fields)
- Experience working with large-scale biological datasets
- Published research or practical experience in scientific AI applications or long‑horizon reasoning
- Background in reinforcement learning and/or pretraining
- Knowledge of containerization technologies (e.g., Docker, Kubernetes) and cloud deployment at scale
- Demonstrated ability to work across multiple domains, such as language modeling, systems engineering, and scientific computing
- Contributions to open-source scientific software or databases
Responsibilities
- Develop novel evaluation frameworks and training strategies to advance AI in biology
- Design and implement rigorous methods to measure and improve model performance on complex scientific tasks
- Collaborate closely with world‑class researchers and engineers to build AI systems across all phases of research and development
- Maintain commitment to safety and beneficial impact while pushing the frontier of what AI can achieve in biology
Technologies
PythonLarge language modelsMachine learning development practicesData pipelines for large-scale datasetsContainerization technologies (Docker, Kubernetes)Cloud deployment at scaleReinforcement learningPretraining
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