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.
What You'll Work OnArchitecting 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 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.
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.
