Physical AI Engineer

Civ Robotics • San Francisco, CA

5h

About The Position

Civ Robotics is seeking a Physical AI Engineer to explore, prototype, and deploy new approaches to intelligence on real robots. This role involves building learning-based perception and vision systems for unstructured outdoor environments, exploring advanced AI techniques like Vision-Language-Action models, foundation models, imitation learning, and reinforcement learning. The engineer will create methods for robots to learn from various data sources, combine classical robotics with modern machine learning, and develop models for scene understanding, localization, manipulation, and autonomous behavior. Responsibilities also include building data pipelines for physical AI, translating research into practical robot applications, designing and testing experiments in simulation and on physical machines, and collaborating closely with cross-functional engineering teams. At the Staff level, this role includes contributing to the long-term Physical AI architecture and identifying future development areas.

Civ Robotics is a company dedicated to automating repetitive tasks in the construction industry through innovative robotics and autonomous navigation. With over 100 robots deployed globally, they provide advanced layout tools for various sectors, aiming to make projects more precise, simple, and efficient.

Requirements

• Deep experience in machine learning / deep learning
• Deep experience in computer vision
• Deep experience in robotics and autonomous systems
• Proficiency in C++ and Python
• Experience with PyTorch or similar ML frameworks
• Experience with ROS / ROS2
• Experience with real-world sensor data: cameras, LiDAR, GNSS, IMU, or similar
• Experience training, evaluating, and deploying models on real systems
• Ability to make things work outside the lab
• Understanding the difference between a model running in a notebook and a model running every day on a robot
• Care about latency, compute, bad sensors, weird edge cases, changing environments, debugging, and the details between an idea and a working machine

Nice To Haves

• Vision-Language Models / Vision-Language-Action models
• Imitation learning or reinforcement learning
• Robot foundation models
• 3D perception
• Neural rendering / NeRF / Gaussian Splatting
• Self-supervised or unsupervised learning
• Synthetic data and simulation
• NVIDIA Jetson / TensorRT / edge inference
• Isaac Sim or other robotics simulators
• Large-scale robotics datasets
• Learning from teleoperation or human demonstrations
• Experience with heavy equipment, autonomous vehicles, agricultural robots, mining, construction, or other outdoor robotics

Responsibilities

• Building learning-based perception and vision systems for unstructured outdoor environments
• Exploring Vision-Language-Action models, foundation models, imitation learning, reinforcement learning, and learned world models
• Creating ways for robots to learn from demonstrations, operators, previous missions, and fleet data
• Combining classical robotics with modern machine learning
• Developing models for scene understanding, terrain understanding, object detection, segmentation, localization, manipulation, and autonomous behavior
• Finding ways to turn large amounts of robot sensor data into useful training data
• Building data collection, evaluation, replay, and training pipelines for physical AI
• Taking recent research and figuring out what actually works on a robot
• Designing experiments, testing them in simulation, and then getting outside and putting them on a machine
• Working closely with autonomy, controls, embedded, mechanical, and product engineers
• Helping define the longer-term Physical AI architecture for our robots and fleet
• Identifying what we should be working on next, not just executing an existing roadmap

Benefits

• Comprehensive healthcare coverage (medical, dental, vision) for you and your family
• Competitive salary with growth potential
• Equity options in a fast-growing robotics company

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed