ML & Cloud Infrastructure Engineer Intern

Gritt 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 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.