Publié le mois dernier
Computer Vision Engineer, 3D
@ Mecka
Richmond Hill, Ontario
En bref
Mecka is hiring a full-time Computer Vision Engineer, 3D to own the classical computer-vision side of its robotics data pipeline. The work covers 3D geometry, temporal filtering, inverse kinematics and delivery QA, with a focus on its 3D hands pipeline. The posting requires 5+ years of experience and strong Python skills, and it lists no salary, schedule or work arrangement.
Salaire :
200 000 $–230 000 $ par année
Responsabilités :
- Own multi-view geometry, calibration and reprojection
- Design temporal filters that remove jitter
- Run inverse kinematics and bone-length constraints
- Build delivery QA metrics and visualizers
- Support classical CV problems across the pipeline
Résumé des exigences :
- 5+ years in software engineering and computer vision
- Strong production Python; C++ a plus
- Solid multi-view geometry and calibration
- Temporal filtering and Kalman-family methods
- Inverse kinematics on articulated structures
- Working knowledge of pose and mesh regressors
Outils et compétences
Source et dates
Site carrière / système de candidatures · ashby
- Date publiée par l’employeur
- 2026-10-01
- Première détection par notre source
- 2026-10-08
- Mise à jour du dossier source
- 2026-10-20
Ces dates décrivent le dossier source, pas une vérification en direct du recrutement. Consultez l’offre originale.
Détails du poste
| Niveau | Senior |
| Domaine | Développement logiciel |
| Expérience | 5 an(s) et plus |
| Heures par semaine | 40 (estimé) |
| Milieu de travail | Bureau |
| Position | Surtout assis |
| Exigences physiques | physique faible · ordinateur élevé |
| Langues | English |
Résumé et détails rédigés par l’IA à partir de l’offre; l’offre de l’employeur fait foi.
Mecka
Profil d’entreprise sauvegardé
Renseignements historiques provenant d’un profil The Jobs API sauvegardé (champs LinkedIn/Crunchbase). Ils ne sont pas vérifiés en temps réel; les chiffres peuvent avoir changé. Les valeurs manquantes restent inconnues.
Data Layer for AI Robotics
Description complète de l’entreprise
Mecka create large scale human data for AI and robotics training
- Abonnés LinkedIn
- 980
- Personnes associées sur LinkedIn
- 38
- Catégories d’activité
- Robotics
- Analytics
- Artificial Intelligence (AI)
- Autres noms enregistrés
- Mecka
- Mecka AI
- Spécialités
- Robotics
- Artificial Intelligence
- and Data
- Le profil indique une agence de recrutement
- Non
- Investissement total déclaré
- 68 000 000
La devise et la date d’observation ne sont pas fournies; ce n’est ni une valorisation actuelle ni une ronde de financement.
Mecka create large scale human data for AI and robotics training
| Champ | Valeur |
|---|---|
| Secteur | Data Infrastructure and Analytics |
| Taille | 2-10 employees |
| Type d’organisation | Privately Held |
| Spécialités | RoboticsArtificial Intelligenceand Data |
| Offres sur Jobsy | 33 |
| Voir la page |
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.
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