Job description

About the Company Civ Robotics is on a mission to automate repetitive tasks within the $3 trillion infrastructure construction industry. We’re dedicated to bridging the workforce gap and accelerating the development of essential infrastructure projects. At the core of our mission is our innovative technology in robotics and autonomous navigation. With over 100 robots deployed and operating across construction sites worldwide, we pride ourselves in ushering in the next generation of construction layout tools for solar, civil, land surveying, road striping, and general contracting. Our technology is engineered from the ground up to make our customers’ projects more precise, simple, and efficient than ever before. Join us and become an integral member of our dynamic team, leading the charge in pioneering construction robotics innovation! About the Role You’ll explore, prototype, and deploy new approaches to intelligence on real robots. That could mean: • 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 rather than treating them as competing philosophies • 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 At Staff level, you'll also help identify what we should be working on next - not just execute an existing roadmap. What we're looking for You have deep experience in some combination of: • Machine learning / deep learning • Computer vision • Robotics and autonomous systems • C++ and Python • PyTorch or similar ML frameworks • ROS / ROS2 • Real-world sensor data: cameras, LiDAR, GNSS, IMU, or similar • Training, evaluating, and deploying models on real systems But more importantly, you know how to make things work outside the lab . You understand that a model running once in a notebook is very different from a model running every day on a robot. You care about latency, compute, bad sensors, weird edge cases, changing environments, debugging, and the thousand small details between a good idea and a working machine. You might be a great fit if • You read new robotics and AI papers and immediately want to try them. • You have side projects involving robots, cameras, drones, arms, cars, embedded systems, or strange pieces of hardware you probably didn't need to buy. • You enjoy moving between research code and a robot with a wrench next to it. • You're comfortable questioning the current approach and saying, “There might be a completely different way to solve this.” • You don't divide the world into “AI people” and “robotics people.” • And when something finally works on a 10,000-pound machine in the real world, that feels considerably more exciting than improving a benchmark by another 1%. Bonus points Experience with any of the following is useful, but not required: • 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 • Heavy equipment, autonomous vehicles, agricultural robots, mining, construction, or other outdoor robotics Why Civ Robotics We have something that is still rare in Physical AI: Real robots, doing real work, generating real data. Our machines are deployed in environments where autonomy creates immediate economic value. That gives us the opportunity to close the loop between deployment, data, learning, and improvement. You'll be able to build something, put it on a robot, test it in the field, learn from it, and make the next version better. We're looking for someone who wants to help figure out what the next generation of real-world robots should look like — and then build it. Benefits & Perks • Comprehensive healthcare coverage (medical, dental, vision) for you and your family. • Competitive salary with growth potential. • Equity options in a fast-growing robotics company.

Requirements

Must-haves

You’ll explore, prototype, and deploy new approaches to intelligence on real robots.
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 rather than treating them as competing philosophies
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

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
Heavy equipment, autonomous vehicles, agricultural robots, mining, construction, or other outdoor robotics

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaipythonml

Platform: gem
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision