Principal Machine Learning Engineer

Responsibilities

• Design, build, and optimize large-scale training pipelines for generative models on biological and chemical data
• Own production ML systems end to end, including model deployment, serving infrastructure, monitoring, and reliability
• Architect ML infrastructure supporting rapid iteration across sequence design and multimodal scientific reasoning
• Drive the engineering side of the Lab-in-the-Loop lifecycle by integrating experimental feedback loops
• Define and advance ML engineering standards, tooling, and best practices
• 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
• 10+ years of hands-on experience building and operating production ML systems at scale
• Deep expertise in distributed training infrastructure and large-scale GPU clusters (AWS, GCP, or on-prem)
• Strong software engineering fundamentals including system design, CI/CD, and observability
• Proficiency in ML frameworks such as PyTorch, JAX, or TensorFlow
• Demonstrated ability to drive technical direction for ML infrastructure independently

Preferred Qualifications

• Experience building infrastructure for generative models applied to biological sequences or molecular structures
• Experience with agentic frameworks, active learning loops, or closed-loop experimental workflows
• Contributions to open-source ML tools or infrastructure projects
• Familiarity with life science domains like molecular biology, genomics, or protein engineering
• Experience with model evaluation frameworks for scientific applications

About the Company

Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously.