Senior / Principal ML Scientist, Foundation Models for Life Sciences
San Francisco, CA$268k - $384kFull-timePosted 5mo ago
About the role
Principal ML Engineer role focused on designing, building, and scaling ML infrastructure for foundation models in life sciences, including structure prediction, co-folding for antibodies and biologics, sequence design, and multimodal scientific reasoning. End-to-end ownership of training pipelines, distributed compute, model deployment, and integration into Lila's closed-loop discovery engine.
Responsibilities
- Design, build, and optimize large-scale training pipelines for structure prediction, co-folding, and other generative models on biological and chemical data, including distributed training across GPU clusters
- Own production ML systems end to end: model deployment, serving infrastructure, monitoring, and reliability for models used in Lila's scientific workflows
- Architect ML infrastructure that supports rapid iteration across structure prediction, sequence design, and multimodal scientific reasoning workloads
- Make structure prediction and co-folding models fast and cheap enough to run at campaign scale, where inference volume is often the bottleneck on scientific throughput
- Drive the engineering side of Lila's "Lab-in-the-Loop" lifecycle: build pipeline models, integrate experimental feedback loops, and ensure model outputs are actionable for downstream scientific workflows
- Define and advance ML engineering standards, tooling, and best practices across the AI organization
- Collaborate with AI scientists to translate research prototypes into robust, scalable production systems, bridging the research-to-deployment gap
Qualifications
- Master's degree or higher in Computer Science, Machine Learning, or a related quantitative field (or Bachelor's with equivalent professional experience)
- Extensive hands-on experience building and operating production ML systems at scale
- Deep expertise in distributed training infrastructure, including experience with large-scale GPU clusters (AWS, GCP, or on-prem)
- Strong software engineering fundamentals: system design, production-grade code, CI/CD, observability, and reliability practices
- Proficiency in ML frameworks (PyTorch, JAX, or TensorFlow) with experience optimizing training and inference performance
- Demonstrated ability to drive technical direction for ML infrastructure independently, from architecture through implementation
- Track record of cross-functional collaboration with research scientists, translating between ML methodology and engineering execution
Skills
Required:Distributed training infrastructureProduction ML systemsML frameworks
Benefits
- Full-time U.S. benefits package including medical, dental, and vision
- Employer-paid life and disability insurance
- Flexible time off with holidays
- Paid parental leave
- Educational assistance program
- Commuter benefits (bike share for office-based employees)
- Company subsidized lunch program
Last updated Aug 26, 2026. Interested? Apply on the company site.
About Lila Sciences
Pioneering Scientific Superintelligence
Pioneering Scientific Superintelligence to solve humankind’s greatest challenges.
Scientific SuperintelligenceAIAutonomous LabsLife SciencesChemical SciencesMaterials Sciences
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