Robot Learning Engineer

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: Robot Learning 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

• Train policies (imitation learning, RL, or fine-tuned foundation models) for real robot tasks.

• Build data collection and evaluation pipelines; run ablations.

• Deploy policies on hardware and iterate against real-world performance.

• Test your work on real robots at the office.

• Get hands-on with pose estimation, VLM, VLA, 3D scene reconstruction, object detection, and multimodal learning.

• Get the opportunity to publish (for PhD interns).

• Attend Tier-1 industry conferences.

An example project could be could be anything from pick-and-place policies for large flexible panels under wind disturbance, to sim-to-real for manipulation from a machine on uneven ground.

What we look for

• Pursuing MS/PhD in ML, Robotics, CS, or related field (exceptional undergraduates welcome).

• Strong PyTorch; solid grounding in deep learning and one of IL/RL.

• Applied evidence: publications, open-source, or substantial course/competition projects.

• Should be comfortable taking ownership of tasks with light supervision.