Job description

ABOUT MECKA AI Mecka AI is building the data infrastructure layer for robotics and embodied AI. We design and operate 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. We work closely with frontier robotics teams to bridge real-world data, simulation, learning-based systems, and deployed hardware. 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. If you don't check every box above but believe you can do the job, we encourage you to apply — we're looking for capability and trajectory, not a perfect checklist match. We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, we will partner with you to meet your needs. We may use AI-assisted tools to help evaluate applications against the requirements of this role. These tools support our recruiting team in identifying qualified candidates but do not replace human judgment or limit which applications may be reviewed or considered.

Requirements

Must-haves

You'll partner closely with our research teams — you need to understand the models, but you won't be training them.
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.
5+ years of professional experience in software engineering and computer vision.
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-haves

Strong software engineering. This is a coding-heavy role: clean, efficient, production Python (C++ is a plus).
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.
If you don't check every box above but believe you can do the job, we encourage you to apply — we're looking for capability and trajectory, not a perfect checklist match.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aigopythonml

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Vision