[Hiring] Senior Computer Vision Engineer @Trace

Role Description

We are hiring a senior computer vision engineer to own the spatial perception layer of our data pipeline – the part of the system that turns raw, sensor-heavy data we capture into aligned, reliable representations the rest of the platform depends on.

This is load-bearing work. If calibration, localization, and trajectory recovery are unreliable, everything downstream – hand and pose annotation, object understanding, scene labeling, policy training – gets worse. Doing this well makes the entire output of Trace better, and our customers feel it immediately.

The work spans calibration, localization, mapping, pose estimation, and the failure modes that show up when you run perception systems against real-world data at scale. The specific sensor stack we capture on today will evolve over time, so we are looking for someone who is comfortable reasoning across software, sensors, and data quality rather than someone tied to a particular pipeline.

What you will do

• Own camera and multi-sensor calibration across our capture rigs, including intrinsics, extrinsics, and time synchronization.
• Build, evaluate, and improve SLAM, VIO, and mapping pipelines that recover aligned 6-DoF trajectories from real-world captures.
• Train and/or fine-tune models for pose estimation and semantic understanding of multi-modal data.
• Diagnose and fix the failures that actually show up in the field – drift, calibration drift, sensor misalignment, degraded tracking, weak reconstructions, noisy data.
• Define the ground-truth and benchmarking methodology we use to know whether the spatial layer is actually getting better.
• Decide where we need custom perception work versus where off-the-shelf components are good enough.
• Work closely with the rest of engineering and with Trace Labs (our applied research arm) to feed reliable spatial outputs into downstream annotation, evaluation, and product workflows.

Qualifications

• Strong experience in at least one of: SLAM, visual odometry, VIO, mapping, or localization.
• Hands-on work with camera calibration, sensor fusion, multi-sensor alignment, or state estimation.
• A track record of shipping perception systems on real hardware, in real-world environments – robotics, autonomy, AR/VR, drones, or other embodied / sensor-heavy systems.
• Comfort reasoning across software, sensors, calibration, and data quality, not just models in isolation.
• Pragmatism about when to use off-the-shelf components, when to build custom, and when to push a problem back to the sensor or capture side.
• High ownership, good judgment, and productive, thoughtful communication.
• Emotional maturity and a collaborative, grounded working style.

Bonus points

• Experience with reconstruction, SfM, pose graph optimization, or bundle adjustment.
• Work on multi-camera systems, LiDAR, or spatial computing.
• Prior exposure to large-scale data capture or sensor-heavy production pipelines.