Research Engineer, Life Sciences

Anthropic•San Francisco, CA

3d•Hybrid

About The Position

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.

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
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-functional teams
Bachelor’s degree or an equivalent combination of education, training, and/or experience
A field relevant to the role as demonstrated through coursework, training, or professional experience
Years of experience required will correlate with the internal job level requirements for the position

Nice To Haves

8+ years of machine learning experience
Prior work experience in AI and biology, including graduate studies (molecular biology, biochemistry, computational biology, or related fields)
Experience working with large-scale biological datasets
Published research or practical experience in scientific AI applications or long-horizon reasoning
Background in reinforcement learning and/or pretraining
Knowledge of containerization technologies (e.g., Docker, Kubernetes) and cloud deployment at scale
Demonstrated ability to work across multiple domains, such as language modeling, systems engineering, and scientific computing
Contributions to open-source scientific software or databases

Responsibilities

Develop novel evaluation frameworks and training strategies that push the frontier of what AI can achieve in biology.
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.
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.