ML & Cloud Infrastructure Engineer Intern
gritt · South San Francisco ·
- Work mode
- Onsite
- Seniority
- Intern
- Employment
- Internship
- Category
- Devops
- Visa sponsorship
- No
- Company size
- 51-200
gritt · South San Francisco ·
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Intern builds and maintains ML training and cloud infrastructure for a robotics startup automating large-scale energy infrastructure construction, using Python, cloud services, and GPU clusters.
Gritt is an intelligent system that combines robotics and AI to build the infrastructure that pulls society forward. Gritt deploys via simple attachments to common equipment found on construction sites and autonomously performs labor-intensive tasks, verification, and planning. Gritt systems are already building critical infrastructure in the harshest outdoor environments, starting with large-scale solar. The founding team includes experts in robotics and AI from Carnegie Mellon, Stanford, and MIT. Gritt is backed by Obvious Ventures, Union Square Ventures, First Round Capital, Climactic, Congruent Ventures, and other leading firms.
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.
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.
Kubernetes, Ray, Spark, Terraform, observability stacks, or ML experiment tooling.
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