Principal ML Research Engineer

Your Impact at LILA Lila is building a platform where AI and automation co-evolve to solve the hardest problems in science. Within Life Science AI (LSAI), we are launching a new AI for Cell Biology team to 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 Principal ML Research Engineer to be the founding engineering leader on this team . This is a 0→1 hands-on role. You will build and operate the engineering platform : domain data, domain-specific models, shared specialist-model serving and inference, agentic infrastructure, and the evaluation harness that the team's research programs run on, and that integrates cell-biology research with Lila's central autonomous-science platform: it's core-model, agentic-systems, and experimental-automation infrastructure that closes the loop between AI reasoning and the lab. You will work closely with Lila's central AI Platform, Data Platform, and autonomous-lab engineering teams to leverage and extend core Lila infrastructure rather than rebuild it , and you will co-develop the technical direction of the team with the VP of AI for Cell Biology and its ML Scientists as you build. Cell- and tissue-scale biology sits at an open frontier of AI for science. The field has produced strong specialist models across sub-domains: single-cell foundation models, molecular structural prediction, perturbation response, cellular imaging, pathway and ligand–receptor inference — but the engineering platform that makes these models reliably composable, the domain data that grounds them, and the evaluation that connects their outputs to autonomous experimentation are still being defined. We have a working point of view on what that platform looks like: domain-specific data curation and accessibility; fine-tuning and (where warranted) training of domain-specific models on cell- and tissue-resolution data; shared specialist-model serving; a unified reasoning-trace and tool-call schema; evaluation-harness instrumentation; and the agentic infrastructure for rollout generation, tool orchestration, and rubric grading that the team's research programs share — and you will refine, challenge, or replace it. The platform choices you make will shape what Lab-in-the-Loop autonomous science looks like at cell and tissue scale. This is a senior IC role for someone who wants to build, with the engineering depth to ship the infrastructure that makes cell-biology research programs thrive and the judgment to co-author tech stack strategy with the team scientific leads as the platform takes shape. What You'll Be Building Build and operate the domain data platform.  Stand up the curation, accessibility, lineage, schema, and versioning infrastructure for the multi-modal scientific data the team's research programs depend on: single-cell, multi-omics, spatial, imaging, perturbation, and genetics. Make complex domain data discoverable and queryable for ML scientists and computational biologists. Steward the reasoning-trace and tool-call schema that lets domain data and downstream traces outlive any single program. Build and operate the shared specialist-model serving and fine-tuning stack.  Serve the field's strongest specialist biology models: single-cell foundation models, structural prediction, perturbation, spatial, imaging, pathway and ligand–receptor models, as composable, versioned tools the team's research programs share. Build the infrastructure to fine-tune these specialists on cell- and tissue-resolution data, and (where warranted by evaluation evidence) to train new domain-specific models. Build and operate the shared agentic infrastructure.  Stand up the rollout-generation, tool-orchestration, rubric-grading, and trace-QC agents that the team's research programs share. Set the standards by which agentic workflows are reproducible, observable, evaluable, and safe to scale. Build and operate the cross-program evaluation harness.  Build the instrumentation that gauges progress across the team's research programs and that connects team-internal metrics to Lila's broader scientific evaluation suite. Benchmarks instrumented here outlive any single program and become part of Lila's standing scientific evaluation suite. Leverage and extend Lila's central AI Platform and Data Platform.  Partner with Lila's central AI Platform, Data Platform, and autonomous-lab engineering teams to extend core Lila infrastructure for cell-biology-specific needs rather than rebuild it. Architect how cell-biology research feeds into and benefits from Lila's foundation-model, agentic-systems, and experimental-automation infrastructure, and how cell-biology research outputs flow back into Lila's broader autonomous-science capability. Push improvements back