Research Scientist – Computer Vision (Body Pose Detection)

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.

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.

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.

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