Anthropic · San Francisco · Hybrid
Research Engineer / Research Scientist, Biology & Life Sciences
9/30/2025
Description
- 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
Qualifications
- Have 8+ years of machine learning experience, with demonstrated ability to train and evaluate large language models
- Have 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
- Have a track record of bridging biological domain knowledge with computational approaches to solve real scientific problems
- Are proficient in Python and familiar with modern ML development practices
- Are comfortable navigating ambiguity and developing solutions in rapidly evolving research environments
- Can work independently while maintaining strong collaboration with cross-functional teams
- Are results-oriented, with a bias towards flexibility and impact
- Thrive in a fast-paced research environment where you balance rigorous scientific standards with rapid iteration
- Are passionate about using AI to accelerate scientific discovery while maintaining high ethical standards
- Have experience managing data pipelines and working with large-scale biological datasets
- Ph.D. in a biological science (molecular biology, biochemistry, computational biology), in Machine Learning, or in a related field, or equivalent industry experience
- Published research or practical experience in scientific AI applications or long-horizon reasoning
- A history working on Reinforcement Learning and/or Pretraining
- Knowledge of containerization technologies (Docker, Kubernetes) and cloud deployment at scale
- Demonstrated ability to work across multiple domains (language modeling, systems engineering, scientific computing)
- Experience with modern machine learning techniques and model training methodologies
- Familiarity with biological databases (UniProt, GenBank, PDB) and computational biology tools
- Experience in drug discovery, including computational chemistry or structure-based design
- Knowledge of regulatory requirements for therapeutic development or clinical research
- Contributions to open-source scientific software or databases
Benefits
$315,000 - $340,000 USD