Postuler

Publié le mois dernier

Research Scientist – Body Pose Detection

@ Mecka

Richmond Hill, Ontario

HybrideTemps plein
Postuler
SIGNAUX

En bref

Mecka is hiring a full-time Research Scientist to build proprietary foundation models for 3D human body tracking and articulated pose estimation for robotics and embodied AI. The work covers architecting and training models from scratch, human-scene interaction modeling, and rapid prototyping of new perception tasks.

Responsabilités :

  • Design and train 3D human pose estimation and mesh recovery networks from scratch
  • Scale multi-view and temporal architectures on multi-GPU clusters
  • Develop loss functions enforcing biomechanical and physical constraints
  • Build models for human-scene interaction and complex motion
  • Prototype solutions for emergent perception challenges

Résumé des exigences :

  • Deep expertise in deep learning and 3D computer vision
  • Experience training large-scale vision models from scratch
  • Knowledge of parametric human body models such as SMPL and SMPL-X
  • Mastery of PyTorch and deep learning scaling frameworks
  • Experience curating multi-terabyte image and video datasets
  • First-author publications at CVPR, ICCV, ECCV or NeurIPS (asset)

Outils et compétences

Source et dates

Site carrière / système de candidatures · ashby

Date publiée par l’employeur
2026-09-28
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

NiveauSenior
DomaineRecherche et sciences
Heures par semaine40 (estimé)
Milieu de travailBureau, Laboratoire
PositionSurtout assis
Exigences physiquesphysique faible · ordinateur élevé
LanguesEnglish

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

ChampValeur
SecteurData Infrastructure and Analytics
Taille2-10 employees
Type d’organisationPrivately Held
SpécialitésRoboticsArtificial Intelligenceand Data
Offres sur Jobsy34
LinkedInVoir la page
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

While our existing perception division handles state estimation and spatial mapping, this role is dedicated to one of the most critical bottlenecks in embodied AI: full-body kinematics, human locomotion, and human-scene interaction. We are hiring a Research Scientist to architect and train proprietary foundation models from scratch focused on 3D human body tracking and articulated pose estimation.

Your core mandate is twofold: building our in-house equivalents to cutting-edge 3D human body and mesh recovery architectures, and developing highly robust interaction models tailored for complex, real-world environments characterized by severe occlusions and dynamic motion. Beyond these core pillars, you will serve as a lead problem-solver for emergent perception challenges as our hardware and downstream robotics needs evolve.

To achieve this, we can provide a massive, continuous stream of high-quality, proprietary ground-truth human motion data captured by our infrastructure. You will use this data advantage to train networks that surpass current public baselines, owning the complete human-scene perception loop for our data engine.

What You'll Work On

Architecting Proprietary Articulation Models

  • Zero-to-One Model Development: Design, implement, and train state-of-the-art networks for 3D human pose estimation, dense full-body mesh recovery, and kinematic tracking.

  • Large-Scale Distributed Training: Scale multi-view and temporal ML architectures across multi-GPU clusters to handle massive, multi-modal datasets of humans navigating and interacting with their environments.

  • Loss & Architecture Innovation: Push the boundaries of current paradigms by developing novel loss functions that enforce biomechanical constraints, temporal smoothness, postural balance, and physical plausibility.

Human-Scene Interaction (HSI) & Complex Motion Modeling

  • Dynamic Scene Understanding: Build and train custom architectures capable of handling extreme motion blur, severe self-occlusion, and multi-person crowding inherent in real-world human behavior.

  • Allocentric & Egocentric Tracking: Use your models to track human bodies through complex spaces, mapping foot-to-ground contact, joint torques, and environmental affordances to provide rich regularization for downstream action-conditioned robotics models (especially humanoid robots).

Emergent Perception R&D

  • Rapid Prototyping: Tackle novel, unmapped AI challenges as they arise. You will rapidly prototype and deploy new models for tasks spanning fine-grained action segmentation, intent prediction, and novel hardware sensor integrations.

  • Agile Problem Solving: Pivot to resolve sudden algorithmic bottlenecks in the data engine, adapting the latest research to unblock new product capabilities for our robotics customers.

Dense Contact & Physics-Aware Tracking

  • Interaction Integration: Connect the outputs of your foundational tracking models into highly optimized pipelines that reason about physical contact surfaces, gravity, and momentum, directly bridging the gap between human video data and robotic control/locomotion policies.

What You Bring

Required Background

  • Deep expertise in Deep Learning, 3D Computer Vision, and specifically Articulated Tracking / Human Body Pose Estimation.

  • Proven experience training large-scale vision models from scratch, not just running inference or fine-tuning existing checkpoints.

  • Strong theoretical and practical understanding of parametric human body models (e.g., SMPL, SMPL-X, GHUM, MHR, SOMA-X), inverse kinematics, and dense mesh estimation.

  • Mastery of PyTorch and deep learning scaling frameworks.

  • Experience handling and curating massive, multi-terabyte image and video datasets for training.

  • Comfortable operating in a fast-paced environment where priorities can shift rapidly to capitalize on new research or hardware capabilities.

Strong Signals:

  • First-author publications in top-tier venues (CVPR, ICCV, ECCV, NeurIPS) focusing on 3D human pose tracking, human-scene interaction (HSI), human motion capture, or human mesh recovery.

  • Specific experience working with massive human motion and interaction datasets (e.g., AMASS, Human3.6M, EgoBody, PROX) and solving the unique optimization challenges they present.

Why This Role?

  • The Data Advantage: You will have access to a scale and quality of proprietary spatial and temporal ground truth for human motion that most academic researchers only dream of.

  • Pure R&D & Model Ownership: You are not maintaining legacy systems; you are given a blank slate and the compute resources to build the state-of-the-art.

  • High Impact: The kinematic priors and interaction models you architect will directly define how the next generation of embodied AI agents—from mobile manipulators to humanoid robots—learn to physically navigate, balance, and interact with the world.

Warning: Research Scientist positions require hyper-specific expertise. Please limit your applications to one research role. Applying to multiple Research Scientist positions suggests a lack of focus and may result in the rejection of all submissions. You may, however, apply to other non-research roles alongside your research application.

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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