Research Engineer, Life Sciences
$350,000–$500,000 year
HybridSan Francisco, California, United States
San Francisco, California, United StatesHybridFull Time$350,000–$500,000 yearSenior LevelBachelors DegreeAI ServicesLarge
Full TimeSenior LevelBachelors DegreeLargeAI Services
Job Summary
Develop novel evaluation frameworks and training strategies to push the frontier of AI in biology, combining machine learning engineering expertise with rigorous methods for measuring model performance on complex scientific tasks. Build and manage data pipelines for large-scale datasets while collaborating with researchers to develop systems that engage across all phases of life sciences R&D. Navigate ambiguity in rapidly evolving research environments to accelerate progress from early discovery through translation.
Required Qualifications
- Demonstrated experience training and evaluating large language models
- Proficiency in Python
- 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
- Ability to work independently
- Ability to collaborate effectively across cross-functional teams
- 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
- Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time
Desired Qualifications
- Previous experience in life sciences
- 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
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