Senior Machine Learning Scientist (Biological Systems) | Hybrid/Remote

About the Role

Lila is building an AI and automation platform to tackle some of the toughest challenges in medicine. In Life Science AI, we develop autonomous-science capabilities across cellular and tissue biology—supporting single-cell omics, perturbation biology, spatial profiling, imaging, genetics, and multi-modal experimental data.

As a Senior Machine Learning Scientist, you’ll help turn our vision for Autonomous Life Science AI into reliable systems. You’ll design architectures, workflows, and evaluation methods that enable rigorous reasoning about biological hypotheses, experiment planning, evidence incorporation, and faster discovery.

Responsibilities

• Build and iterate autonomous life science system components for hypothesis reasoning and experimental planning
• Translate scientific direction into working architectures, pipelines, and evaluation approaches
• Develop Bayesian and methodology-driven frameworks for integrating evidence and updating beliefs
• Create robust evaluation strategies to measure scientific rigor, quality of proposed hypotheses, and experimental outcomes
• Collaborate with cross-functional teams combining deep biological expertise with foundation modeling and agentic systems

Requirements

• Strong background in machine learning with experience applying it to scientific or biological domains
• Demonstrated understanding of Bayesian reasoning and scientific methodology
• Experience designing ML workflows and evaluation methods for complex, data-driven systems
• Ability to work with multi-modal biological data (e.g., omics, imaging, spatial or genetic data)
• Proven track record building research-grade systems that can be operationalized

Benefits

• Opportunity to work on frontier autonomous-science problems in medicine
• Collaboration with experts across life science, ML, and automation
• Impactful role shaping how AI systems reason, propose experiments, and accelerate discovery