8+ years experience in robotics systems development, robot learning, or AI/ML for physical autonomous systems, with experience in manipulation, locomotion, or mobile robotics
Deep understanding of physical system dynamics, kinematics, and the engineering constraints that govern real-world robot behaviour
Hands-on ML capability: able to build, validate, and deploy ML solutions independently in Python using modern ML frameworks
Proficient in working directly in JupyterHub, VS Code, Marimo, and W&B Models to build and deploy customer-facing applications
Working knowledge of ML approaches relevant to robotics: imitation learning, reinforcement learning, sim-to-real transfer, anomaly detection for physical systems, or trajectory prediction
Able to validate ML solutions on physical grounds and identify when a model output violates the constraints of the real system it represents
Proven experience leading complex technical customer engagements, managing multi-stakeholder environments, and maintaining executive relationships
Familiarity with robot simulation environments (Isaac Sim, MuJoCo, Gazebo, or similar) and their role in training and validating robot learning pipelines
Able to translate field observations into structured product signals that are actionable for an engineering team
Bachelor’s or Master’s degree in Robotics, Mechanical Engineering, Electrical Engineering, Computer Science, Physics, or a related technical discipline
Experience deploying robot learning systems on real hardware, including managing the sim-to-real gap in production settings
Familiarity with foundation models for robotics and their application to generalised manipulation or locomotion tasks
Experience working with compute-intensive training workloads and an understanding of the infrastructure requirements they create
Experience identifying and progressing expansion opportunities within strategic customer accounts
Track record of contributing to internal knowledge frameworks, technical publications, or industry forums in the robotics space
Familiarity with ROS/ROS2 and associated tooling
Wondering if you’re a good fit?
We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams — even if you aren’t a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk
You understand why the same neural network that works in simulation can fail on hardware, and you find solving that problem an interesting challenge
You want to build AI solutions that run on real robots in real environments, not in controlled demonstrations
You are equally comfortable discussing learning algorithm design with an ML researcher and actuator dynamics with a mechanical engineer
You want to bring your robotics domain expertise into a team that already knows how to land and expand AI solutions with engineering customers, and you see being the domain authority that opens a new vertical as a genuinely compelling opportunity