Research Scientist, Life Sciences
San Francisco, CA
Posted 16d ago
Remote Work Policy
On-site
Categories
AI Research Engineer
About the job
Anthropic is seeking an exceptional Research Scientist to join its Life Sciences team. This role focuses on making Claude a superhuman life sciences research assistant, operating at the intersection of machine learning, software engineering, and biology. You will directly improve model capabilities on scientific tasks through post-training, evaluation design, and RL environment development. As a core member, you will translate deep biological domain knowledge into model training objectives, benchmarks, and agentic workflows, helping establish Anthropic as a leader in AI-accelerated biology and shaping how frontier models reason about computational biology tasks. This is a unique opportunity to shape how frontier AI models learn biology, working alongside top AI researchers on problems crucial for human health and scientific understanding.
Responsibilities
- Build and ship agentic tools and integrations for life science workflows like bioinformatics pipelines, database queries, analysis notebooks, and literature review.
- Design and build evaluation benchmarks to measure model capabilities on biology tasks, including figure interpretation, bioinformatics, protocol reasoning, and literature synthesis.
- Collaborate with product and design teams to scope, prototype, and ship features for life sciences users.
- Partner with external biotech, pharma, and academic users to understand workflows and incorporate feedback into product improvements.
- Build and maintain the engineering infrastructure for the biology product surface, including tool scaffolding, data pipelines, and eval harnesses.
- Translate biological domain knowledge into product requirements and evaluation criteria to guide model improvement.
Requirements
- Experience applying ML and software engineering to biological problems (e.g., computational biology, bioinformatics, protein ML, genomics).
- Experience in drug discovery/development at a biotech/pharma company or fundamental research in an academic setting, with an understanding of scientific workflows.
- Strong software engineering skills, including production-quality Python, working in large codebases, and end-to-end infrastructure ownership.
- Hands-on experience training or fine-tuning ML models (LLMs, protein language models, or other deep learning architectures).
- A track record of shipping computational tools or pipelines used by biologists.
- Comfort navigating ambiguity and defining problems in a rapidly evolving research environment.
- Ability to work independently while collaborating closely with research, product, and domain-expert teams.
- Results-oriented with a bias toward rapid iteration and measurable impact.
- Passion for using AI to accelerate scientific discovery with high ethical standards.