Research Engineer - Machine Learning for Robot Planning

About this role

Overview Arm is extending its “Arm everywhere” vision into robotics and embodied AI—where intelligence must operate under real-world constraints of latency, power, and safety. We are building a new robotics research lab in Austin focused on the science of integration—understanding how different paradigms (learning, control, planning) work together in real robotic systems, and how they map onto heterogeneous compute. This role focuses on how robot capabilities are realized through machine learning, and how those capabilities behave when deployed in full systems

You will develop and evaluate ML-based approaches—including VLA/VLM models, RL, and learning-based planning—and study their interaction with other components in hierarchical robotic systems. The goal is not just better models, but deployable intelligence under real-world constraints. Responsibilities Develop ML methods for perception, planning, and control in robotics Work with VLM/VLA models and embodied AI systems Benchmark learning-based approaches against classical and hybrid methods Integrate ML components into hierarchical, multi-layer robotic systems Contribute to system-level prototypes and demonstrations Translate ML insights into system and compute requirements Required Skills and Experience PhD or equivalent experience in ML, Robotics, or related field Strong experience in RL, imitation learning, or embodied AI Proficiency in Python and ML frameworks (e.g., PyTorch, JAX) Experience with robotic systems or simulation environments Publications in top-tier venues “Nice To Have” Skills and Experience Experience with task/motion planning Familiarity with foundation models for robotics Experience with sim-to-real transfer In Return You will help define how learning-based intelligence is realized and deployed in real robotic systems—shaping both Arm’s research direction and the architectures that will power robots at global scale

#LS-KS1 Salary Range: $161,500-$218,500 per year We value people as individ

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