Research Engineer - Robot Perception and Sensor Fusion

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 perception as a system-level capability, including how sensing, representation, and understanding integrate into full robotic stacks

You will design perception pipelines that operate efficiently under real-world constraints and connect seamlessly with planning, reasoning, and control layers across heterogeneous compute systems. Responsibilities Develop perception systems (SLAM, sensor fusion, semantic mapping) Design multi-modal sensing pipelines and environment representations Integrate perception with planning, reasoning, and control systems Evaluate performance under real-world constraints (latency, bandwidth, robustness) Contribute to system-level integration and demonstrations Translate perception workloads into platform-level requirements Required Skills and Experience Strong background in robotics perception (SLAM, vision, sensor fusion) Experience with real-world robotic systems or datasets Proficiency in C++ and/or Python Familiarity with modern perception and mapping techniques Publications in top-tier venues “Nice To Have” Skills and Experience Experience with multi-modal learning (vision, lidar, tactile) Familiarity with edge deployment and optimization Experience integrating perception with full robot stacks In Return You will define how robots perceive and represent the world as part of integrated intelligence systems—shaping both Arm’s research direction and the architectures that will power robots at global scale. #LS-KS1 Salary Range: $198,100-$268,000 per year We value people as individuals and our dedication is

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