Senior ML Scientist, Biological Systems

Lila Sciences•San Francisco, CA

10h•Onsite

About The Position

Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), we develop autonomous-science capabilities for cellular and tissue biology, spanning single-cell omics, perturbation biology, spatial profiling, imaging, genetics, and multi-modal experimental data that integrate deep biological expertise with foundation modeling and agentic systems. We are seeking a Senior Machine Learning Scientist to help execute this vision by building autonomous life science systems grounded in epistemology, scientific methodology, Bayesian argumentation, and automation. This role will translate the scientific direction of Autonomous Life Science AI into working architectures, workflows, and evaluation methods that allow AI systems to reason rigorously about biological hypotheses, propose experiments, incorporate evidence, and accelerate discovery. This is a hands-on scientific and technical role for someone who can operate at the intersection of machine learning, biological reasoning, agentic systems, and experimental design. The right person will be comfortable formalizing how scientific knowledge is represented, how uncertainty is handled, how evidence changes belief, and how automated systems can execute increasingly rigorous cycles of life science discovery.

Requirements

PhD in Computer Science, Machine Learning, Computational Biology, Statistics, Biology, or a related quantitative field.
Strong research track record in machine learning, AI for science, computational biology, probabilistic modeling, agentic systems, or a related area.
Deep understanding of scientific reasoning, experimental design, uncertainty, and evidence integration.
Experience building or researching systems that reason over complex scientific, biological, or experimental data.
Strong foundation in modern ML methods, with hands-on experience in frameworks such as PyTorch, JAX, or TensorFlow.
Ability to translate biological questions into computational and ML problems, and to translate ML system behavior back into scientific terms.
Strong technical judgment, with the ability to operate in open-ended research settings where the right architecture, abstraction, or evaluation method is not yet obvious.
Excellent collaboration skills across AI, biology, automation, and platform teams.

Nice To Haves

Experience with Bayesian modeling, probabilistic programming, causal inference, or formal methods for reasoning under uncertainty.
Experience building agentic, active-learning, closed-loop, or autonomous-science systems.
Familiarity with biological data modalities such as single-cell omics, perturbation data, imaging, spatial profiling, genetics, or multi-omics.
Experience designing systems that generate, rank, test, or revise scientific hypotheses.
Background in philosophy of science, epistemology, scientific methodology, or formal argumentation.
Experience integrating computational predictions with experimental or automated lab workflows.
Track record of publishing or presenting work at premier ML, computational biology, or scientific venues.

Responsibilities

Build autonomous life science systems that connect AI reasoning, biological evidence, experimental design, and automated execution.
Translate the broader Autonomous Life Science AI vision into concrete architectures, workflows, prototypes, and production-quality research systems.
Develop methods for representing hypotheses, uncertainty, evidence, and scientific arguments in ways that enable robust machine reasoning.
Apply Bayesian reasoning, epistemology, and scientific methodology to the design of AI systems that can propose, test, and revise biological hypotheses.
Design agentic workflows that plan experiments, reason over results, and close the loop between computational predictions and automated laboratory feedback.
Partner with ML scientists, experimental scientists, automation teams, and platform teams to ensure systems are biologically grounded and experimentally actionable.
Build evaluation frameworks for autonomous discovery systems, including benchmarks for reasoning quality, hypothesis generation, evidence integration, and experimental utility.
Contribute to the technical roadmap for autonomous life science research systems and help raise the scientific rigor of the team’s approach.