Research Scientist - Computer Vision (Body Pose Detection)

About the role

Mecka AI is building the data infrastructure layer for robotics and embodied AI. This role focuses on one of the most critical bottlenecks in embodied AI: full‑body kinematics, human locomotion, and human‑scene interaction. As a Research Scientist you will architect and train proprietary foundation models from scratch for 3D human body tracking and articulated pose estimation, leveraging a massive, continuous stream of high‑quality, proprietary ground‑truth human motion data.

What You’ll Work On

• Architecting Proprietary Articulation Models: 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: Develop novel loss functions that enforce biomechanical constraints, temporal smoothness, postural balance, and physical plausibility.
• Human‑Scene Interaction (HSI) & Complex Motion Modeling: Build custom architectures capable of handling extreme motion blur, severe self‑occlusion, and multi‑person crowding; track bodies through complex spaces, mapping foot‑to‑ground contact, joint torques, and environmental affordances.
• Emergent Perception R&D: Rapidly prototype and deploy new models for 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.
• Dense Contact & Physics‑Aware Tracking Integration: Connect foundational tracking outputs into pipelines that reason about physical contact surfaces, gravity, and momentum, bridging human video data and robotic control/locomotion policies.

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 fine‑tuning).
• 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, 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 their unique optimization challenges.

Why This Role?

• The Data Advantage: 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: Build state‑of‑the‑art models from a blank slate with ample compute resources, without maintaining legacy systems.
• High Impact: The kinematic priors and interaction models you create 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.