Research Engineer / Research Scientist, Biology & Life Sciences

Details

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems that are safe and beneficial for users and society. The Life Science team seeks to accelerate progress across the life sciences (from early discovery through translation) by combining deep biological expertise with machine learning engineering. In this founding-team role, you will work at the intersection of cutting-edge AI and biological sciences to develop evaluation frameworks, training strategies, and models that advance scientific discovery while maintaining strong safety and ethical standards.

Responsibilities

• Design and implement evaluation methodologies for assessing AI model capabilities relevant to biological research and applications.
• Develop and execute strategies to systematically improve model performance on scientific tasks.
• Develop approaches to address long-horizon task completion and complex reasoning challenges essential for scientific discovery.
• Collaborate with domain experts and partners to establish benchmarks and gather high-quality data.
• Translate between biological domain knowledge and machine learning objectives.

Requirements

• 8+ years of machine learning experience, with demonstrated ability to train and evaluate large language models.
• 5+ years of hands-on experience in life sciences R&D, with deep expertise in areas such as molecular biology, drug discovery, or computational biology.
• Proven track record of bridging biological domain knowledge with computational approaches to solve real scientific problems.
• Proficiency in Python and familiarity with modern ML development practices.
• Experience managing data pipelines and working with large-scale biological datasets.
• Comfortable navigating ambiguity and developing solutions in rapidly evolving research environments; able to work independently while collaborating cross-functionally.
• Results-oriented, flexible, and able to balance rigorous scientific standards with rapid iteration.
• Passion for using AI to accelerate scientific discovery while maintaining high ethical standards.
• Minimum education: Bachelor’s degree in a related field or equivalent experience (PhD preferred/beneficial but not strictly required).

Preferred / Strong Candidates May Have

• Ph.D. in a biological science (molecular biology, biochemistry, computational biology), Machine Learning, or a related field, or equivalent industry experience.
• Published research or practical experience in scientific AI applications or long-horizon reasoning.
• Experience with Reinforcement Learning and/or Pretraining.
• Knowledge of containerization technologies (Docker, Kubernetes) and cloud deployment at scale.
• Demonstrated ability to work across multiple domains (language modeling, systems engineering, scientific computing).
• Familiarity with biological databases (UniProt, GenBank, PDB) and computational biology tools.
• Experience in drug discovery, including computational chemistry or structure-based design.
• Knowledge of regulatory requirements for therapeutic development or clinical research.
• Contributions to open-source scientific software or databases.

Benefits & Logistics

• Expected base annual salary: $315,000 - $340,000 USD. Total compensation includes equity, benefits, and may include incentive compensation.
• Location: San Francisco, CA. Location-based hybrid policy: staff are expected to be in one of Anthropic's offices at least 25% of the time.
• Visa sponsorship: Anthropic will make reasonable efforts to sponsor visas for selected candidates and retains an immigration lawyer to assist.
• Application deadline: rolling; no specific deadline provided.

Why This Role Matters

This role offers an opportunity to shape how AI transforms biological research by developing rigorous evaluation methods and training strategies. You'll collaborate with leading researchers and engineers to build AI systems that can engage in research and development phases while upholding safety and beneficial impact for human health and scientific understanding.