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Lila Sciences

AI-powered scientific discovery for medicines

Senior Data Engineer - Multiple Teams

Data EngineeringConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USAIn-person

About the Job

Requirements

  • Two to six years of experience in data engineering, bioinformatics, cheminformatics, or computational science.
  • Strong Python skills, including typed, tested, production-quality code.
  • Strong SQL skills, especially with Postgres or similar relational databases.
  • Experience building ETL pipelines, data models, and reusable data transformations.
  • A data science foundation, including statistics and pandas, NumPy, or similar tools.
  • Experience translating noisy scientific measurements into accurate, validated datasets.
  • Workflow orchestration experience, ideally with Flyte, Airflow, Prefect, Dagster, or Nextflow.
  • Active use of AI coding tools in day-to-day engineering work.

Responsibilities

  • Design pipelines that turn raw lab output into analysis-ready scientific data.
  • Model heterogeneous data from biology, chemistry, and materials instruments.
  • Build validation checks, schema-evolution gates, and data quality workflows.
  • Develop reusable analysis functions for scientific and AI research workflows.
  • Improve automation and observability across instrument-to-result data flows.
  • Build canonical datasets that scientists and AI researchers can trust.
  • Use AI coding tools to accelerate pipeline development and team velocity.
  • Partner with AI researchers and experimentalists to turn raw lab instrument outputs into validated, analysis-ready datasets.
  • Transform messy, per-instrument measurements into clean, well-typed data that is efficient to query, reliable to use, and ready for downstream analysis.
  • Build domain-specific analysis functions and reusable data pipelines to help scientists and AI researchers avoid re-deriving bespoke solutions.

Desired Qualifications

  • Experience with columnar or lakehouse stacks such as Parquet, Iceberg, DuckDB, Polars, or Ibis.
  • Familiarity with event-driven pipelines such as NATS or Kafka.
  • Exposure to lab instrument data formats, LIMS, or ELN systems.
  • Familiarity with life sciences assays, sequencing, imaging, or flow cytometry.
  • Familiarity with materials or chemistry methods such as XRD, XRF, SEM, TGA, or DSC.
  • Experience with curve fitting, peak detection, or unit and dimensional analysis.

About the Company

Lila Sciences

AI-powered scientific discovery for medicines

Lila.ai uses artificial intelligence to speed up scientific discovery in medicines and advanced materials. The system ingests large sets of scientific data, experimental results, and literature to identify patterns, generate hypotheses, and guide research programs. It differentiates itself by tackling deep scientific problems at scale and combining AI with domain expertise, rather than offering generic tools. The goal is to help humanity by delivering new medicines and materials that solve major biological and materials challenges through data-driven discovery.

Company size
501-1,000
Company stage
Series A
Total funding
$550M
Headquarters
Cambridge, MA
Founded
2024

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