Research Engineer / Research Scientist, Biology & Life Sciences

Details

Anthropic is building reliable, interpretable, and steerable AI systems. The Life Science team aims to accelerate progress across the life sciences by combining deep biological domain knowledge with modern machine learning engineering. In this role you will work at the intersection of AI and biology to develop evaluation frameworks, training strategies, and models that advance scientific discovery while maintaining a strong focus on safety and beneficial impact.

Responsibilities

• Design and implement evaluation methodologies to assess AI model capabilities relevant to biological research and applications.
• Develop and execute strategies to systematically improve model performance on scientific tasks, including long-horizon task completion and complex reasoning.
• Translate biological domain knowledge into machine learning objectives and vice versa.
• Collaborate with domain experts and external partners to establish benchmarks and gather high-quality biological data.
• Manage and work with large-scale biological datasets and data pipelines to support model training and evaluation.
• Contribute to cross-functional engineering and research efforts to build robust, scalable model training and deployment workflows.

Requirements

• 8+ years of machine learning experience, including demonstrated ability to train and evaluate large language models and modern model training methodologies.
• 5+ years of hands-on life sciences R&D experience (molecular biology, drug discovery, computational biology or similar).
• Proficiency in Python and familiarity with modern ML development practices.
• Experience managing data pipelines and working with large-scale biological datasets.
• Comfortable navigating ambiguity and iterating rapidly in research environments; able to work independently while collaborating closely with cross-functional teams.
• Results-oriented mindset with strong communication skills and a commitment to ethical, safety-conscious AI development.

Strong candidates may have

• Ph.D. in a biological science, machine learning, or a related field, or equivalent industry experience.
• Published research or practical experience applying AI to scientific problems, especially long-horizon reasoning tasks.
• Experience with Reinforcement Learning and/or pretraining approaches.
• Knowledge of containerization technologies (Docker, Kubernetes) and cloud deployment at scale.
• Familiarity with biological databases (UniProt, GenBank, PDB) and computational biology tools.
• Experience in drug discovery (computational chemistry or structure-based design) or knowledge of regulatory requirements for therapeutic development or clinical research.
• Contributions to open-source scientific software or databases.

Logistics & Benefits

• Education: At least a Bachelor's degree in a related field or equivalent experience required.
• Location policy: Location-based hybrid — staff expected to be in an office at least ~25% of the time; some roles may require more office presence.
• Visa sponsorship: Anthropic does sponsor visas where feasible and retains immigration counsel to support hires.
• Compensation: Competitive base salary, equity, benefits, and potential incentive compensation.
• Work environment: Collaborative research-focused culture valuing high-impact, large-scale AI research and strong communication.

If you are excited about applying ML to accelerate biological discovery and meet many of the qualifications above, Anthropic encourages you to apply even if you do not meet every listed qualification.