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Matterworks is building foundation models that decode biological dark matter—the vast majority of mass spectrometry signals that go unidentified in typical experiments. The company's Large Spectral Models apply AI to biochemical discovery, similar to how AlphaFold transformed protein prediction.
As a Data Engineer, you will own end-to-end data pipelines that power both the AI training infrastructure and the product layer. You'll acquire mass spectrometry and molecular data at scale, transform it into clean, reproducible datasets, and embed quality checks that prevent bad data from shipping. Your work directly enables the ML team's training corpora and ensures the product can safely explain results to customers.
Key responsibilities include:
- Designing, testing, instrumenting, and operating production pipelines on a schedule
- Building data quality checks that compare each build against the previous version and halt on failure
- Ingesting and validating new public and partner datasets in messy scientific formats
- Creating consistent schemas, trustworthy metadata, and documented data definitions
- Growing from owning well-scoped pipelines toward owning larger platform components
You'll report to the Head of Engineering and collaborate daily with ML researchers, scientists, and the product team. The role is hands-on with a clear growth path in an early-stage startup.
Required: 2+ years building production data pipelines, proficiency in Python and SQL, working knowledge of cloud data infrastructure. The team uses Argo Workflows, Metaflow, EKS, Glue, Athena, Apache Iceberg, Parquet, DuckDB, and Terraform. You should have demonstrated experience owning pipelines end-to-end (including tests, monitoring, and failure handling), comfort with messy data formats, and clear written communication skills. Life sciences or biotech experience is a plus but not required; you'll work alongside strong in-house chemistry expertise daily.
