Robot learning engineer

Get AI-powered advice on this job and more exclusive features.

Compensation range: $120,000 - $300,000 (FT); $20-$30 per hour (Co-op)

Mundane is a venture-backed seed-stage robot learning startup founded by a team of Stanford researchers and builders. We’re deploying a massive fleet of humanoid robots to perform mundane tasks in commercial environments, collecting data to build the next generation of embodied intelligence. We’re a fast-paced, execution‑driven team of engineers, roboticists, and dreamers.

You’ll develop the learning systems that give our robots adaptability in the real world. From data pipelines to training and deployment of ML models, you’ll push the frontier of embodied intelligence by making robots learn directly from large-scale, real-world experience.

At Mundane, your code won’t sit in simulation — it will be trained, tested, and deployed on humanoids operating in customer environments. This is a high-impact role at the intersection of machine learning and robotics.

• Design and implement ML algorithms for robot perception, control, and decision-making.
• Build and maintain data pipelines for large-scale collection, labeling, and training from robots in the field.
• Develop real-time inference systems that integrate seamlessly with the robotics stack.
• Collaborate with controls, perception, and embedded teams to deliver robust end-to-end autonomy.
• Optimize models for reliability, latency, and performance on hardware‑constrained platforms.
• Push forward new approaches in robot learning — imitation learning, RL, foundation models for robotics.

• Strong proficiency in Python and experience with ML frameworks (PyTorch, Tensor Flow, JAX).
• Hands‑on experience with robotic software stacks (ROS2, real‑time control, perception pipelines).
• Background in reinforcement learning, imitation learning, or embodied AI.
• Experience deploying ML models to real-world systems, not just simulation.
• Strong debugging skills across hardware, software, and data.
• Bonus:
  Publications in robotics/ML conferences (RSS, ICRA, CoRL, NeurIPS), or experience scaling large data‑driven systems.

• Direct ownership of the learning systems powering humanoid robots.
• Early equity with meaningful upside in a venture‑backed robotics company.
• The ability to ship ML models to live robots in weeks, not quarters.
• Exposure to the full robotics stack — hardware, controls, perception, embedded systems.
• A front‑row seat in scaling a technically ambitious company from seed stage.