Computer Vision Engineer, 3D

The role

We're looking for a strong, coding-heavy computer vision engineer to own the classical side of our stack. Our models produce raw per-frame predictions; you own the traditional computer vision, temporal filtering, and inverse kinematics — backed by excellent software engineering — that turn those into clean, rigid, smooth, correctly-calibrated spatial data we can deliver.

You'll partner closely with our research teams — you need to understand the models, but you won't be training them.

Your immediate focus is our 3D hands pipeline, where this work is needed most today. From there you'll be our classical-CV specialist across the organization, brought in wherever geometry, filtering, or pipeline problems come up.

What You'll Do

• Own the classical 3D geometry that places reconstructions correctly in space: multi-view geometry, triangulation, PnP, camera calibration, undistortion, and reprojection.
• Design and tune temporal filters (low-pass, Kalman-family, interpolation) that remove jitter without losing real motion.
• Run inverse kinematics and enforce physical constraints such as bone-length rigidity so reconstructed skeletons stay valid. Build the checks that keep bad frames out of delivery.
• Turn model output into clean delivered data, including per-frame results, reports, and the QA metrics that gate delivery, and keep the pipeline running at scale.
• Build visualizers and dashboards to inspect trajectories, overlays, and failure cases.
• Be the go-to engineer for classical computer-vision problems across the pipeline as they come up.
• go-to for classical computer-vision problems elsewhere in the pipeline as they come up.

What You Bring
Must-have:

• 5+ years of professional experience in software engineering and computer vision.
• Strong software engineering. This is a coding-heavy role: clean, efficient, production Python (C++ is a plus).
• Solid multi-view geometry: triangulation, PnP, camera calibration, reprojection, and coordinate transforms.
• Temporal filtering and signal processing: low-pass and Kalman-family filters, interpolation, and a feel for the trade-off between smoothness and fidelity.
• Inverse kinematics and rigid-body or bone-length constraints on articulated structures.
• Enough ML to work with deep models such as pose and mesh regressors, including their outputs and failure modes, without training them yourself.
• A data-quality mindset: comfortable with noisy real-world data and building QA that catches problems before customers do.

Nice-to-have:

• Human pose or mesh reconstruction (hand or body), including keypoint and mesh pipelines.
• Depth in Bayesian filtering or state estimation; experience with optimization frameworks such as Ceres or GTSAM.
• Building CV tooling such as trajectory visualizers and overlay or QA dashboards.
• Stereo, multi-view, or ego-exo capture experience.
• Light temporal ML, such as small networks for smoothing or gap-filling.