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AnthropicAnthropicSan Francisco, CA

Research Engineer, Life Sciences

Research Engineer developing novel evaluation frameworks and training strategies for AI systems in life sciences and biology. Requires experience training/evaluating LLMs, Python/ML proficiency, and data pipeline expertise; biology background preferred but not required.

350k – 500k
Hybrid8+ YOEML Engineering

About the 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

Education

  • 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

Compensation

Annual Salary: $350,000—$500,000 USD

Skills

LLMsPythonMachine LearningData PipelinesReinforcement LearningPretrainingDockerKubernetesBiological DatasetsScientific Computing

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