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-functional teams
Preferred Qualifications
- 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
The annual compensation range for this role is listed below.
For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
Logistics
Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at le
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