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
Location: SF Bay Area (in-person)
About the role
We’re looking for an experienced Robot Learning Engineer to join our team. As an early member, you will play a pivotal role in building and shaping the AI capabilities of our robots. You will work on challenging problems around manipulation and navigation in dynamic outdoor environments. You'll need to thrive in a fast-paced startup environment where you'll wear multiple hats and have a direct impact on our product's evolution. Ideally, you have a proven track record of developing and deploying physical AI systems in production, and you're passionate about pushing the boundaries of what's possible in robotics with AI.
What you’ll get to work on
Help drive our technical approach, with particular focus on real-world deployments.
Research and develop techniques around RL, imitation learning, video-action and world models to achieve robust generalization across tasks and environments.
Build approaches for pre/post training of large robotics models.
Work with multimodal data (e.g. cameras, LIDAR, tactile).
Develop and improve real/sim data pipelines, training and validation infrastructure.
Collaborate with other teams in the company.
What we look for
M.S/Ph.D degree in robotics, vision, computer science, mechanical engineering, electrical engineering or other engineering disciplines (or equivalent experience).
4+ years of hands-on experience developing AI-driven solutions for robotics applications like manipulation and navigation.
Extensive experience in computer vision, machine learning, reinforcement learning or other relevant areas of AI (as evidenced by industry experience or publication record).
Experience with approaches like VLAs, deep RL, imitation learning, diffusion, world models.
Extensive experience with Python and common frameworks like PyTorch.
