Anthropic is seeking to create reliable, interpretable, and steerable AI systems to accelerate progress in the life sciences, from early discovery through translation, by an order of magnitude, while maintaining safety and beneficial impact.
Requirements
- 8+ years of machine learning experience, with demonstrated ability to train and evaluate large language models
- 5+ years of hands-on experience in life sciences R&D, with deep expertise in areas such as molecular biology, drug discovery, or computational biology
- Proficient in Python and familiar with modern ML development practices
- Experience with containerization technologies (Docker, Kubernetes) and cloud deployment at scale
- Knowledge of biological databases (UniProt, GenBank, PDB) and computational biology tools
- Experience with modern machine learning techniques and model training methodologies
- Familiarity with Reinforcement Learning and/or Pretraining
Responsibilities
- Design and implement evaluation methodologies for assessing AI model capabilities relevant to biological research and applications
- Develop and execute strategies to systematically improve model performance on scientific tasks
- Develop approaches to address long-horizon task completion and complex reasoning challenges essential for scientific discovery
- Collaborate with domain experts and partners to establish benchmarks and gather high-quality data
- Translate between biological domain knowledge and machine learning objectives
- Develop novel evaluation frameworks and training strategies that push the frontier of what AI can achieve in biology
- Manage data pipelines and work with large-scale biological datasets
Other
- 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
- Visa sponsorship: we do sponsor visas, but we aren't able to successfully sponsor visas for every role and every candidate
- Strong communication skills
- Ability to work independently while maintaining strong collaboration with cross-functional teams
