Principal, Machine Learning Engineer at Lila Sciences · LumiJobs

Responsibilities

Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), Machine Learning engineers build and operate the systems that turn foundation models over biological sequence, molecular structure, and experimental data into production capabilities powering automated scientific discovery across Lila's life science domains.
We are seeking a Principal ML Engineer to design, build, and scale the ML infrastructure behind those models. Much of the team's current work is in structure prediction and co-folding for antibody and biologics design, alongside sequence design and multimodal scientific reasoning. You will own critical systems end to end, from training pipelines and distributed compute to model deployment and integration into Lila's closed-loop discovery engine.
This is a high-impact IC role for someone who operates at the intersection of ML systems engineering and life science applications. You will shape the technical direction for how ML models are trained, evaluated, and deployed at scale, collaborate closely with AI scientists and experimental researchers to close the computational–experimental loop, and drive Lila's ML infrastructure toward the next generation of capabilities.
What You'll Be Building
Design, build, and optimize large-scale training pipelines for structure prediction, co-folding, and other 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 structure prediction, sequence design, and multimodal scientific reasoning workloads
Make structure prediction and co-folding models fast and cheap enough to run at campaign scale, where inference volume is often the bottleneck on scientific throughput
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

Principal, Machine Learning Engineer

Responsibilities

Qualification

Required

Preferred

Benefits

Company

Lila Sciences

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