About the RoleYou'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 forYou 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 pointsExperience 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
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.
