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Lila is building an AI-driven drug discovery factory that closes the loop between computational design and wet-lab experimentation in days instead of months. As Senior Engineer for the Drug Discovery Platform, you will own the data backbone that enables this closed loop: the molecule queue, compound registration system, synthesis constraints, and experimental result capture pipelines that feed back into predictive models.
You will design and operate the shared molecule queue that brokers compounds between AI Scientists and the Make-Test platform, managing status state machines, batch grouping, and priority semantics. You'll build the canonical molecule registry with SMILES/InChI normalization, stereochemistry handling, salt/parent resolution, duplicate detection, and stable internal IDs that all downstream systems depend on.
You'll create pipelines ingesting data from ChemSpeed automated synthesis, QC instruments (LCMS, NMR), and bioassay readers, capturing identity, purity, dose-response curves, IC50/EC50, ADMET measurements, and selectivity panels into queryable stores. You'll maintain the live synthesis constraints and inventory state that the Batch Assembly AI reads each cycle, covering building-block inventory, advanced precursors, ChemSpeed capacity, stock alerts, and chemistry-specific constraints.
You'll own data quality, lineage, schema evolution, and SLAs for the closed-loop cycle time target of a few days from computational proposal to experimental truth.
Required: Bachelor's or Master's in Computer Science, Chemistry, Computational Biology, or related field with 5+ years building data platforms in production. You need production experience designing and shipping data platform components (ingestion frameworks, registries, storage abstractions, orchestration), fluency in backend APIs/services, Python, and SQL. Strong database and schema design skills with relational and/or NoSQL databases, comfort with structured/semi-structured/unstructured data, and experience with AWS and containerized deployment (Kubernetes) are essential. You should be comfortable modeling chemical and biological data and understand the messy reality of experimental measurements.
Bonus experience includes cheminformatics (RDKit, OpenEye), compound registration systems (CDD Vault, Dotmatics, Benchling), ELN/LIMS integration, workflow orchestration (Flyte, Airflow, Dagster, Temporal), lakehouse patterns (Iceberg, Delta Lake), automation/robotics data capture, pharma/biotech domain background, and agentic/LLM workflow infrastructure.
