Software Engineer, Data Platform
Posted 5 Aug 2026
Advertised as “Senior Software Engineer, Data Platform”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Employment
- Hybrid
- Contract
- Full-time
No salary listed for this role.
About Us
Most of the molecules driving human biology are invisible to us. Mass spectrometers already detect metabolites, lipids, and peptides, but the vast majority of those signals never get identified. A typical experiment names a small fraction of its features and discards the rest. We call this biology's dark matter. It’s signal-rich and mechanism-defining, yet almost entirely opaque.
Matterworks is building the foundation models that make that dark matter legible. Our Large Spectral Models do for biochemical biology what AlphaFold and ESM did for proteins: turning a library-bound discipline into something predictable and generative, and embedding it at every stage of R&D.
Come build the future of biological discovery with us.
Position Overview
As a Senior Software Engineer you’ll work to build the connective tissue of our data platform, in both what you build and how you build it. Design, build and scale systems to enrich data from raw samples and information into readily usable datasets enriched with biological context. The data produced by you and the team will serve our customers through both our ML research and model development activities as well as our product.
You will report to the Head of Engineering and work daily with our machine learning researchers, scientists, and product team.
Key Responsibilities
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Build and Scale Data Contracts: Own the pipelines and system other teams consume from. Design and implement systems that scale to multiple petabytes of data in an effective way.
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Serving and Cost at Scale: You will build and scale systems to acquire, store and serve data in a fast and affordable as it grows: data layout, featurization throughput, Kubernetes-native orchestration, and cost surfaced before it adds up.
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Labels and Enrichment: Turn raw data into datasets people can use, with consistent schemas, trustworthy metadata, and documented definitions. Scale scientific labels from studies down to their spectra and underlying features.
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Quality Gates: Automate quality checks that enable increasing capability without regression and promote only on a pass.
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Interfaces People Use: Own the surfaces AI, chemistry, product, and agents call, from the SDK used to build datasets to the tools that expose platform capabilities.
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Operations and Data Rights: Ensure effective operations of our data needs meeting our designed service level agreements, while providing high quality, provenance, and secure data processing in line with our customer needs.