Outdoor Robotics AI Engineer for Real-World Deployment

Gritt; South San Francisco (CA); On-site; USD 140,000 - 200,000; Full time

Job summary

Gritt seeks an experienced Robot Learning Engineer to shape the AI capabilities of its autonomous robots deployed on outdoor construction environments. You will address manipulation and navigation in dynamic settings, contributing to end-to-end system deployment and real-world testing.

You will collaborate cross-functionally to advance physical AI, with a focus on scalable data pipelines, production-ready models, and robust generalization across tasks and environments.

Qualifications

• M.S./Ph.D in robotics or related field is required.
• 4+ years developing AI for robotics (manipulation/navigation).
• Strong background in AI/ML with CV/RL or related areas.
• Experience with Python and PyTorch.
• Legally authorized to work in the United States.

Responsibilities

• Develop and deploy state-of-the-art physical AI models for manipulation and navigation tasks.
• Help drive our technical approach for real-world deployments.
• Research and develop techniques around RL, imitation learning, video-action and world models.
• Build strategies for pre/post training of large robotics models.
• Work with multimodal data (cameras, LIDAR, tactile).
• Develop and improve real/sim data pipelines, training and validation infra.

Skills

Python

PyTorch

Reinforcement learning

Imitation learning

World models

Multimodal data handling

Education

M.S./Ph.D in robotics, vision, computer science, or related engineering

Job description

Gritt seeks an experienced Robot Learning Engineer to shape the AI capabilities of its autonomous robots deployed on outdoor construction environments. You will address manipulation and navigation in dynamic settings, contributing to end-to-end system deployment and real-world testing.

You will collaborate cross-functionally to advance physical AI, with a focus on scalable data pipelines, production-ready models, and robust generalization across tasks and environments.