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Mecka AI

Labeled human movement data for AI

Research Scientist - Body Pose Detection

Computer VisionUpdated on Nov 12
Full-timeCA$150k - CA$200k /yrHybrid

About the Job

Requirements

  • Deep expertise in deep learning, 3D computer vision, and articulated tracking or human body pose estimation.
  • Proven experience training large-scale vision models from scratch, rather than only running inference or fine-tuning existing checkpoints.
  • Strong theoretical and practical understanding of parametric human body models such as SMPL, SMPL-X, GHUM, MHR, and 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.
  • Ability to operate in a fast-paced environment where priorities can shift rapidly to capitalize on new research or hardware capabilities.

Responsibilities

  • Design, implement, and train state-of-the-art networks for 3D human pose estimation, dense full-body mesh recovery, and kinematic tracking.
  • Scale multi-view and temporal machine-learning architectures across multi-GPU clusters to handle massive, multi-modal datasets of humans navigating and interacting with their environments.
  • Develop novel loss functions that enforce biomechanical constraints, temporal smoothness, postural balance, and physical plausibility.
  • Build and train custom architectures capable of handling extreme motion blur, severe self-occlusion, and multi-person crowding in real-world human behavior.
  • Track human bodies through complex spaces, mapping foot-to-ground contact, joint torques, and environmental affordances to provide regularization for downstream action-conditioned robotics models, especially humanoid robots.
  • Rapidly prototype and deploy new models for tasks including fine-grained action segmentation, intent prediction, and novel hardware sensor integrations.
  • Adapt the latest research to resolve sudden algorithmic bottlenecks in the data engine and unblock new product capabilities for robotics customers.
  • Integrate foundational tracking model outputs into optimized pipelines that reason about physical contact surfaces, gravity, and momentum, bridging human video data and robotic control or locomotion policies.
  • Own the human-scene perception loop for the data engine and develop human-scene interaction models for complex, real-world environments.

Desired Qualifications

  • First-author publications in top-tier venues such as CVPR, ICCV, ECCV, or NeurIPS focused on 3D human pose tracking, human-scene interaction, human motion capture, or human mesh recovery.
  • Experience working with large human motion and interaction datasets such as AMASS, Human3.6M, EgoBody, or PROX, and solving their optimization challenges.

About the Company

Mecka AI

Labeled human movement data for AI

Mecka AI builds custom, labeled datasets of human movement by recording people performing specific tasks and analyzing the video to create multimodal data for AI and robotics training. Its offerings are data-as-a-service: bespoke, high-quality video data that is anonymized, meticulously annotated, and ready for direct ingestion into machine learning models. The company differentiates itself with a global, on-demand data-collection network and task-focused labeling to supply precise, task-specific datasets for autonomous systems, including humanoid robotics partners. Its goal is to accelerate the development and deployment of embodied AI and autonomous technologies by providing scalable, ready-to-use movement data.

Company size
51-200
Company stage
Series B
Total funding
$120M
Headquarters
New York City, New York
Founded
2025

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