Robot Learning Engineer

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: 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

• Develop and deploy state-of-the-art physical AI models for challenging manipulation and navigation problems.

• 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.

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

• Must have excellent problem-solving skills.

• Legally authorized to work in the United States.