Research Engineer

Requirements:

• MS / PhD in computer vision, robotics, or ML, or equivalent published research output.
• You have trained a perception model that beat an off-the-shelf baseline on a real domain and shipped it. Data curation, loss design, and evaluation decisions were yours.
• Deep hands-on work in at least two of: 3D hand/human pose reconstruction, stereo or monocular depth, VIO/SLAM, open-vocabulary detection and segmentation, and video VLMs.
• Multi-view geometry is fluent, not looked up: rectification, epipolar residual, Q matrix and depth sign conventions, triangulation, and world vs. camera frame.
• You can read a calibration report and say whether the problem is the camera or the model and be right.
• Strong Python and PyTorch; you own training and inference code, not notebooks.
• Production inference experience on AWS: containerization, batch pipelines, orchestration.
• 4+ years on problems in this space.

Nice-to-Have:

• Publications in egocentric vision, 3D hand pose, VLA / VLM models, or robot learning.
• IMU-synced multimodal data, gravity alignment, and camera-IMU extrinsics.
• Robotics data formats: MCAP / Foxglove, ROS, LeRobot, RLDS.
• Worked with Ego4D, EgoExo4D, Open X-Embodiment, or comparable large egocentric corpora.
• Open-source contributions to vision or robotics projects.