Senior / Principal ML Scientist, Foundation Models for Life Sciences

The role

You will design, build, and scale ML infrastructure for foundation models used in life sciences. This includes managing training pipelines, distributed compute, and model deployment for scientific discovery.

What you will do

• Design, build, and optimize large-scale training pipelines for generative models on biological and chemical data
• Own production ML systems including model deployment, serving infrastructure, monitoring, and reliability
• Architect ML infrastructure for structure prediction, sequence design, and multimodal scientific reasoning workloads
• Make structure prediction and co-folding models fast and cheap for campaign scale
• Build pipeline models and integrate experimental feedback loops for the Lab-in-the-Loop lifecycle
• Define and advance ML engineering standards, tooling, and best practices
• Collaborate with AI scientists to translate research prototypes into production systems

Required 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
• Track record of cross-functional collaboration with research scientists

Nice to have

• Experience building training or inference infrastructure for generative models applied to biological sequences, molecular structures, or scientific data
• Experience supporting structure prediction or co-folding workloads, including AlphaFold-derived methods, diffusion models, or protein language models
• Experience with agentic frameworks, active learning loops, or closed-loop experimental workflows
• Contributions to open-source ML tools, frameworks, or infrastructure projects
• Familiarity with at least one life science domain (structural biology, protein engineering, molecular biology, genomics, or nucleic acid design)
• Experience with model evaluation frameworks for scientific applications where ground truth is sparse or delayed

Benefits

• 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 for office based employees
• Company subsidized lunch program

About the company

Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. The company combines advanced AI models with proprietary AI Science Factory™ instruments to execute the scientific method autonomously.