An exceptional research engineer will join Anthropic’s Life Sciences team to accelerate progress in life sciences through AI. The role involves developing innovative evaluation frameworks and training strategies for large language models, measuring and improving model performance on complex biological tasks, and collaborating with top researchers and engineers to build AI systems that maintain safety and beneficial impact.
Research Engineer, Life Sciences en Anthropic
Híbrido - San Francisco, CA
Más vacantes en AnthropicSalary
USD 350,000 - 500,000
Requirements
Skills
- 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
- 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
- Maintain the team’s commitment to safety and beneficial impact
Technologies
PythonDockerKubernetescloud deployment at scalelarge language modelsdata pipelinesmodern ML development practices
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