ML & Cloud Infrastructure Engineer Intern

Job description

Gritt https://gritt.ai/ is developing physical AI to automate the construction of large-scale infrastructure around the globe. Gritt’s systems are already deployed commercially in difficult outdoor environments, and are helping to build critical energy infrastructure. The founding team https://www.gritt.ai/team comprises experts in robotics and AI from Carnegie Mellon, Stanford and MIT. Gritt is a Series A company backed by marquee VCs. Role: ML & Cloud Infrastructure Engineer Intern Location: SF Bay Area (in-person) About Internships at Gritt Our internships are scoped projects: you own a defined deliverable end-to-end, work with a dedicated mentor, and demo your work to the whole team. Many interns receive return or full-time offers. This will be an internship for one of two durations: 3 months, or 6 months. We offer competitive salaries, and the opportunity to work on a mission with tremendous climate impact. What you'll get to work on - Build and operate training, data, or evaluation infrastructure used daily by the wider SW team. - Work with GPU clusters, orchestration, and data pipelines at scale. - Instrument, monitor, and harden the pipeline you ship. - Test your work on real robots at the office. - Opportunity to publish (for PhD interns). - Attend Tier-1 industry conferences. An example project could be anything from an auto-curation pipeline that mines fleet logs for rare events (gusts, occlusions, near-misses) to feed training, to a regression harness that replays field scenarios against each new model release. WHAT WE LOOK FOR - Pursuing BS/MS/PhD in CS or related field. - Strong Python; familiarity with cloud services (AWS/GCP), containers, and CI/CD. - Evidence of building infrastructure or data systems. - Should be comfortable taking ownership of tasks with light supervision. - Must have excellent problem-solving skills. - Legally authorized to do an internship in the United States for either 3 months or 6 months. NICE TO HAVE - Kubernetes, Ray, Spark, Terraform, observability stacks, or ML experiment tooling.