Research Engineer (Life Sciences)

TL;DR

Research Engineer (Life Sciences) (AI/ML): Developing novel evaluation frameworks and training strategies for large language models to measure and improve performance on complex scientific tasks in biology, with an accent on rigorous model evaluation and training for long-horizon scientific reasoning. Focus on building AI systems that support multiple phases of research and development while maintaining safety and beneficial impact.

Location: San Francisco, CA (hybrid; expected in office at least 25% of the time)

Salary: $350,000 - $500,000 USD annually

Company

Anthropic builds reliable, interpretable, and steerable AI systems with a focus on safety and beneficial impact.

What you will do

• Train and evaluate large language models for complex biological and scientific tasks.
• Develop evaluation frameworks and training strategies to push model performance in biology.
• Build data pipelines for large-scale scientific datasets.
• Collaborate with researchers and engineers to design AI systems across the research and development lifecycle.
• Work in an ambiguous, rapidly evolving research environment and translate findings into practical solutions.

Requirements

• 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.
• Strong written and verbal communication skills; ability to work independently and collaborate cross-functionally.
• Minimum education: Bachelor’s degree or equivalent combination of education, training, and/or experience; required field of study relevant to the role.

Nice to have

• 8+ years of machine learning experience.
• Experience in AI and biology (e.g., molecular biology, biochemistry, computational biology) and/or graduate studies.
• Background in reinforcement learning and/or pretraining.
• Experience with large-scale biological datasets and scientific AI applications (published research or practical work).
• Knowledge of containerization and cloud deployment at scale (e.g., Docker, Kubernetes).

Culture & Benefits

• Hybrid policy: expected to be in one of the offices at least 25% of the time (some roles may require more).
• Generous vacation and parental leave; flexible working hours.
• Competitive compensation and benefits, with optional equity donation matching.
• Visa sponsorship available; reasonable efforts to support visas when an offer is made.

Hiring process

• Application review with emphasis on communication and research impact.
• Interview process may include evaluation of fit for ML research engineering and life sciences work.