Principal Machine Learning Engineer
About the role
Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves. LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy.
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: model deployment, serving infrastructure, monitoring, and reliability for models used in Lila's scientific workflows
- Architect ML infrastructure that supports rapid iteration across sequence design, structure prediction, and multimodal scientific reasoning workloads
- 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
Requirements
Master's degree or higher in Computer Science, Machine Learning, or a related quantitative field (or Bachelor's with equivalent professional experience)
10+ years of 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
Qualifications
Experience building training or inference infrastructure for generative models applied to biological sequences, molecular structures, or scientific data
Bonus points for 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 (molecular biology, genomics, protein engineering, or nucleic acid design)
Skills
Master's degree or higher in Computer Science, Machine Learning, or a related quantitative field (or Bachelor's with equivalent professional experience)
10+ years of 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
Benefits
Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.