Primary record

Research Engineer, Life Sciences

Anthropic Indexed employerSan Francisco, CA · San Francisco, California, United States
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Workplace

hybrid

Employment

Internship

Published

Aug 11, 2026

Closes

No date supplied

The role

About Anthropic

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Role

We're seeking an exceptional Research Engineer to join our Life Sciences team at Anthropic. Our team is organized around the north star goal of accelerating progress in the life sciences, from early discovery through translation, by an order of magnitude. Our team likes to think across the whole model stack. In this role, you'll combine your deep expertise in machine learning engineering to develop novel evaluation frameworks and training strategies that push the frontier of what AI can achieve in biology.

You'll work at the intersection of cutting-edge AI and the biological sciences, developing rigorous methods to measure and improve model performance on complex scientific tasks. You'll collaborate closely with world-class researchers and engineers to build AI systems that can engage in all phases of research and development, while maintaining our commitment to safety and beneficial impact.

Previous experience in life sciences is welcome, but not required for this role.

Minimum Qualifications

• 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

• Comfortable navigating ambiguity and developing solutions in rapidly evolving research environments

• Strong written and verbal communication skills, with the ability to work independently while collaborating effectively across cross-funct

Requirements

Department: AI Research & Engineering