Research Engineer, Life Sciences

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. Annual Salary: $350,000—$500,000 USD 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 least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position ope

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.

Develop novel evaluation frameworks and training strategies to push the frontier of what AI can achieve in biology. Build and refine methods to measure and improve model performance on complex scientific tasks. Collaborate closely with world-class researchers and engineers to build AI systems that engage in all phases of research and development, while maintaining safety and beneficial impact. Work across the whole model stack and apply deep expertise in machine learning engineering to life sciences challenges.