Platform Engineer
A Platform Engineer in this setting typically builds and runs the shared infrastructure that lets researchers train and deploy models at scale, with strong Linux, cloud, container and automation skills. A strong candidate brings solid software engineering habits, comfort with ML tooling and pipelines, and the judgment to keep a research platform reliable and fast. Tying it to an AI materials search engine, the work supports heavy compute and data flows behind turning wanted properties into synthesizable designs.
How it fits the AI era
Platform work is shifting as AI assists with provisioning, monitoring and routine fixes, so the engineer spends more time on design and less on repetitive toil. What stays human is judgment about reliability, cost and tradeoffs, plus understanding what researchers actually need from the platform. In a company compressing materials discovery, a dependable platform is what makes fast model iteration possible.
How it builds AI experience
This role builds practical AI experience through running the compute, storage and pipelines that train and deploy models at scale. Candidates can strengthen it by working with ML infrastructure tools, container orchestration and experiment tracking on real projects. Familiarity with scientific or simulation data flows helps, given the focus on chemical designs and physical properties.
Worth showing
- Linux and cloud basics
- Containers and orchestration
- Automation and scripting
- Reliability and monitoring
This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.
About CuspAI
An AI search engine for new materials that the company says turns a wanted set of physical properties into synthesizable chemical designs in months instead of years of lab trial and error.
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