LILA has created the world's first Operating System for Science powered by Scientific Superintelligence™. Backed by General Catalyst.
About the role
We're building an AI-driven drug discovery factory that closes the loop between computational design and wet-lab experiment in days instead of months. The bottleneck for that loop is data: every compound designed, synthesized, QC'd, and tested needs to be registered, tracked, and surfaced back to our AI Scientists with low latency and high fidelity.
What they're looking for
- Cloud & Infrastructure: Experience with AWS and containerized deployment (Kubernetes)
- AI-Assisted Development: Proficient with AI-assisted development tools (Cursor, Claude Code, or similar) and incorporates them effectively into day-to-day engineering work
More about this role
We're building an AI-driven drug discovery factory that closes the loop between computational design and wet-lab experiment in days instead of months. The bottleneck for that loop is data: every compound designed, synthesized, QC'd, and tested needs to be registered, tracked, and surfaced back to our AI Scientists with low latency and high fidelity.
As the Senior Engineer for the Drug Discovery Platform, you will own the data backbone that makes this closed loop possible: the molecule queue, the compound registration system, the synthesis constraints, and the experimental result capture pipelines that feed back into our predictive models. Your work will directly determine how fast our AI learns from each cycle, and therefore how fast we deliver new medicines.
- Molecule Queue: Design and operate the shared queue that brokers compounds between AI Scientists and the Make-Test platform, including the status state machine, batch grouping, priority semantics, and the read/write contracts both sides depend on.
- Compound & Structure Registration: Build the canonical molecule registry, including SMILES/InChI normalization, stereochemistry handling, salt/parent resolution, duplicate...
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