Senior / Principal ML Scientist, Foundation Models for Life Sciences
$268k - $384k • San Francisco, CA USA • Full-time
Posted 6mo ago
This job has been open for more than 60 days. It may no longer be actively hiring, so check with the company before putting a lot into an application.
Job Location
San Francisco, CA USA
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
Lila Sciences is building a platform where AI and automation co-evolve to solve complex problems in medicine. Within Life Science AI, Machine Learning engineers build and operate systems that transform foundation models into production capabilities for automated scientific discovery. This Principal ML Engineer role focuses on designing, building, and scaling the ML infrastructure behind these models, with current work in structure prediction, co-folding, sequence design, and multimodal scientific reasoning. You will own critical systems end-to-end, from training pipelines and distributed compute to model deployment and integration into Lila's discovery engine, shaping the technical direction for ML model training, evaluation, and deployment at scale.
Responsibilities
- Design, build, and optimize large-scale training pipelines for generative models on biological and chemical data, including distributed training across GPU clusters.
- Own production ML systems end-to-end, including model deployment, serving infrastructure, monitoring, and reliability.
- Architect ML infrastructure to support rapid iteration for structure prediction, sequence design, and multimodal scientific reasoning workloads.
- Optimize structure prediction and co-folding models for speed and cost-efficiency at campaign scale.
- Drive the engineering side of the 'Lab-in-the-Loop' lifecycle, building pipeline models and integrating experimental feedback.
- 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.
Requirements
- 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.
- 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 independently drive technical direction for ML infrastructure from architecture through implementation.
- Track record of cross-functional collaboration with research scientists.
Benefits
- Competitive base compensation with bonus potential
- Generous early-stage equity
- Medical, dental, and vision coverage
- Employer-paid life and disability insurance
- Flexible time off with generous company wide holidays
- Paid parental leave
- Educational assistance program
- Commuter benefits, including bike share memberships
- Company subsidized lunch program