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# Robot Learning Research Engineer (Manipulation Policies)

- **Company:** WOVEN
- **Location:** Palo Alto, CA, United States
- **Salary:** $62,400 - $83,200
- **Contract:** Permanent contract
- **Skills:** Big Data, Learning Management Systems, Software Debugging, Python (Programming Language), Machine Learning, Tensorflow, Reinforcement Learning, Pytorch, Machine Learning Operations, Data Pipelines
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25197_ec8f9fd6f5597c458dd7ba6592182d86

## About the Role

* Strong proficiency in Python and experience with ML frameworks (PyTorch, TensorFlow, 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.

## Description

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., * 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., * 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.