Design and deploy real-time spatial perception systems that allow humanoid and mobile robots to understand, navigate, and interact with complex 3D environments. You will work at the intersection of classical geometry, state estimation, and modern learning-based scene representations to build long-horizon world models that directly power manipulation and Vision-Language-Action (VLA) systems in the real world.
Requirements
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01MS or PhD in Robotics, Computer Vision, Computer Science, or a related field—or equivalent industry experience
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02Strong background in SLAM, state estimation, and probabilistic sensor fusion
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03Deep understanding of 3D geometry, multi-view geometry, camera models, and calibration
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04Hands-on experience building perception systems for real robotic platforms
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05Experience with neural scene representations such as NeRFs, neural occupancy grids, or implicit SDFs
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06Proficiency in Python and C++ in Linux-based robotics environments
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07Experience working with large-scale datasets and long-running perception or learning experiments
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08Self-driven, systems-oriented, and excited about deploying perception on real robots
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09(+) Familiarity with Vision-Language-Action (VLA) or embodied AI systems
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10(+) Experience with tactile, force, or event-based sensors
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11(+) Background in Gaussian splatting, neural SDFs, or hybrid geometric-learning map representations
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12(+) Experience integrating perception outputs into manipulation or control pipelines
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13(+) Familiarity with Isaac Sim, MuJoCo, or photorealistic simulation environments
Details & responsibilities
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01Design and deploy real-time SLAM and state-estimation systems for humanoid and mobile robots