Senior Robotics Engineer

We’re looking for a Senior Robotics Perception Engineer to build end-to-end spatial perception systems that combine multi-camera vision, IMU data, and learning-based models into a unified 3D understanding of the world.

You’ll work on problems spanning SLAM, 6DoF pose estimation, multi-device sensor fusion, and calibration, while also leveraging modern computer vision and ML techniques (e.g., monocular depth, action/skill understanding, VLM).

This role sits at the intersection of robotics, 3D computer vision, and applied ML.

What You’ll Work On

• Build real-time perception pipelines combining:
• Multi-camera systems (head-mounted + wrist-mounted cameras)
• IMU + RGB fusion for accurate camera pose estimation
• Develop and optimize SLAM / visual-inertial odometry (VIO) systems
• Design multi-device sensor fusion to align multiple viewpoints into a single scene
• Implement 3D / 6DoF hand and object pose estimation from RGB / RGB-D inputs
• Implement object detection models
• Work on stereo + multi-view geometry pipelines
• Build robust camera calibration systems:
• Intrinsics / extrinsics
• Cross-device calibration
• Integrate or research ML models for:
• Monocular depth estimation
• Action / skill labeling
• VLM systems
• Optimize pipelines for real-time performance and robustness

What We’re Looking For

• Core Skills (Must-Have)Strong foundation in 3D Computer Vision & Geometry
• Multi-view geometry, epipolar geometry, transformations
• Experience with SLAM / VIO / sensor fusion
• Visual SLAM, visual-inertial fusion, state estimation
• Hands-on experience with camera calibration
• Intrinsics, extrinsics, stereo calibration
• Experience working with multi-camera systems
• Strong programming skills in C++ and/or Python

Good to Have (High Impact)

• Experience with hand pose / human pose estimation (2D/3D/6DoF)
• Familiarity with RGB-D / depth sensors
• Experience with learning-based vision models
• Monocular depth
• Pose estimation
• Action recognition
• Exposure to VLM or embodied AI systems
• Experience optimizing for real-time systems (latency, memory, throughput)
• Familiarity with frameworks like:
• OpenCV, PyTorch, ROS, COLMAP, ORB-SLAM, OpenVINS, etc.

Nice to Have (Bonus)

• Experience with multi-device synchronization (time alignment, sensor clocks)
• Background in robotics, AR/VR, or embodied AI systems
• Experience deploying models on edge devices / mobile systems

What Makes This Role Unique

• You’ll work on complex multi-sensor setups (not just single-camera CV)
• Ownership of end-to-end perception stack (not just modeling or infra)
• Blend of classical geometry + modern ML
• Opportunity to shape next-gen embodied / spatial AI systems

Ideal Candidate Profile

Someone who:

• Can move fluidly between math, systems, and ML
• Is comfortable debugging real-world sensor noise and calibration issues
• Has built or worked on real-time perception systems, not just offline models