Research Engineer, Scientific Data & Workflow Infrastructure
Your Impact at LILA
Lila is building an operating system for science, powered by scientific superintelligence — generating hypotheses, designing and running experiments, and learning from new data in real time to speed up discovery in fields like therapeutics and advanced materials.
The Molecular Intelligence team builds ML and physics-based infrastructure for drug discovery: biophysics workflows, computational chemistry tools, cofolding models, low-data learning systems, simulation workflows, and agent-usable scientific pipelines. This role sits embedded with scientists, working closely with Lila’s platform teams on the storage, compute, and orchestration layers beneath it. You’ll build the provenance system that connects every scientific input, code version, model, run, and result produced across Lila’s simulation, ML, and agent-driven workflows — so a scientist can find a result months later, trace exactly how it was produced, and reuse it without reconstructing it from logs.
What You'll Be Building
- Shared provenance infrastructure — a queryable, trusted record connecting inputs, code/model versions, runs, and outputs across simulation, ML, and agent workflows.
- Data models and metadata schemas for runs, artifacts, and results that work acr
