Matterworks

Platform Engineer

• Build and scale data contracts, pipelines, and systems consumed by other teams across multiple petabytes of data
• Acquire, store, and serve data efficiently and affordably at scale
• Design data layouts and improve featurization throughput
• Implement Kubernetes-native orchestration and surface costs proactively
• Transform raw data into usable datasets with consistent schemas, trustworthy metadata, and documented definitions
• Scale scientific labels from studies to spectra and underlying features
• Automate quality checks and publish data only after passing quality gates
• Own SDKs and tools used by AI, chemistry, product, and agent interfaces
• Operate data services against designed service-level agreements
• Ensure high-quality, provenance-aware, and secure data processing
• Collaborate daily with machine learning researchers, scientists, and the product team
• Report to the Head of Engineering

Senior Software Engineer, Data Platform

Data Engineer

• Own well-scoped data pipelines end to end, including design, testing, instrumentation, documentation, and scheduled operation
• Turn raw mass spectrometry and molecular data into usable datasets with consistent schemas, trustworthy metadata, and documented definitions
• Design, build, and extend data quality checks that compare each build against the previous one before publication
• Stop pipelines when quality checks fail instead of shipping bad data
• Ingest new public and partner datasets by fetching, converting, validating, and reconciling them with existing data
• Improve systems continuously to handle messy scientific and vendor formats at scale
• Build datasets for AI training corpora and product or agentic-layer use
• Report to the Head of Engineering and collaborate daily with machine learning researchers, scientists, and the product team
• Grow from owning well-scoped pipelines and datasets toward owning larger parts of the data platform