Research Engineer, Life Sciences

About the Role We're seeking an exceptional Research Engineer to join our Life Sciences team at Anthropic. 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 will 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. 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 Annual Compensation $350,000 – $500,000 USD per year. Location & Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience. Field of study: Relevant to the role as demonstrated through coursework, training, or professional experience. Minimum years of experience: Correlates with the internal job level requirements. Location-based hybrid policy: Staff are expected to be in one of our offices at least 25% of the time; some roles may require more. Visa sponsorship: We sponsor visas when possible. Equal Opportunity Statement All qualified applicants will receive consideration and we encourage candidates from all backgrounds to apply. #J-18808-Ljbffr Anthropic