Find Jobs
Find Jobs Near You – Available Work in Your Location
Skip to job details
LS
Lila Sciences
Senior Data Engineer, Bioinformatics, Cheminformatics, Materials
Career Insights for Biomedical Engineer (General)
See where this job fits in the broader career landscape. Knowing your career path helps you see what's possible from here.
Scorecard
Based on California data
Review key factors to help you decide if this role fits your goals. How is this calculated?
What they do
A Biomedical Engineer designs solutions to problems in medicine and biology to improve patient care. Combines engineering with medical and biological knowledge. May develop medical products designed to replace biological functions, such as prosthetic limbs or artificial hearts, or design equipment such as X-rays and surgical tools.
$113,160 / year median in California
+4% projected growth
Job Description
Search jobs Explore companies My job alerts Senior Data Engineer, Bioinformatics, Cheminformatics, Materials Lila Sciences Software Engineering, Data Science San Francisco, CA, USA USD 144k-240k / year + Equity Posted on Sep 2, 2026 Apply now Your Impact at LILA Lila's mission is to accelerate scientific discovery with AI, and that depends on trustworthy scientific data. As a Data Engineer, you'll build ETL pipelines and data models for Lila's scientific data platform, working at the intersection of data engineering, computational biology, chemistry, and materials science. You'll partner with AI researchers and experimentalists to turn raw lab instrument outputs into validated, analysis-ready datasets. The core challenge is data modeling: transforming messy, per-instrument measurements into clean, well-typed data that is efficient to query, reliable to use, and ready for downstream analysis. You'll also build domain-specific analysis functions and reusable data pipelines that help scientists and AI researchers move faster without re-deriving bespoke solutions. What You'll Be Building
- Design pipelines that turn raw lab output into analysis-ready scientific data.
- Model heterogeneous data from bio, 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. What You'll Need to Succeed
- 2-6 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.
- Data science foundation, including statistics and pandas, NumPy, or similar tools.
- Experience translating noisy scientific measurements into accurate, validated datasets.
- Workflow orchestration experience, ideally Flyte, Airflow, Prefect, Dagster, or Nextflow.
- Active use of AI coding tools in day-to-day engineering work. Bonus Points For
- 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.