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Mecka AI is building the data infrastructure layer for robotics and embodied AI, designing and operating global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems.
You will own the classical computer vision side of the stack, transforming raw per-frame model predictions into clean, rigid, smooth, correctly-calibrated spatial data. Your work bridges real-world data, simulation, learning-based systems, and deployed hardware. You'll partner closely with research teams—you need to understand the models but won't be training them. Your immediate focus is the 3D hands pipeline, where this work is most critical today. From there, you'll serve as the classical-CV specialist across the organization, brought in for geometry, filtering, and pipeline problems.
Key responsibilities include:
- Own classical 3D geometry for placing 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 on reconstructed skeletons
- Build quality checks that keep bad frames out of delivery
- Turn model output into clean delivered data, including per-frame results, reports, and QA metrics that gate delivery
- Keep the pipeline running at scale
- Build visualizers and dashboards to inspect trajectories, overlays, and failure cases
- Serve as the go-to engineer for classical computer-vision problems across the pipeline
Requirements:
Must-have:
- 5+ years of professional experience in software engineering and computer vision
- Strong software engineering fundamentals; this is a coding-heavy role requiring 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 understanding of smoothness-vs-fidelity trade-offs
- Inverse kinematics and rigid-body or bone-length constraints on articulated structures
- Enough ML knowledge to work with deep models such as pose and mesh regressors, including their outputs and failure modes, without training them
- 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
