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Mecka AI is building the data infrastructure layer for robotics and embodied AI, designing and operating global systems for data capture, labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems.
In this Research Scientist role, you will architect and train proprietary 3D reconstruction pipelines from scratch, transforming Mecka's continuous stream of high-quality, real-world multi-modal data into highly accurate, photorealistic digital environments. You will work at the intersection of neural rendering, spatial intelligence, and robotics, bridging the simulation-to-reality gap.
Key responsibilities include:
• Advanced Neural Reconstruction: Design, train, and scale state-of-the-art models for dense 3D geometry extraction (e.g., Gaussian Splatting, NeRFs) across multi-GPU clusters to handle massive spatial datasets.
• Sim-to-Real Integration: Reconstruct high-fidelity, physically accurate digital twins of real-world environments to provide rich spatial priors that directly improve and regularize downstream robotics pipelines, world models, and other sim-to-real training.
• Client Delivery & Viewer Optimization: Compress, optimize, and package massive 3D scene representations for high-framerate rendering in client-facing viewers, ensuring clients can interact with data without sacrificing visual fidelity.
• Emergent Perception R&D: Rapidly prototype new architectures to isolate dynamic actors from static environments, resolve algorithmic bottlenecks, and fuse novel hardware sensor integrations.
You will have access to proprietary spatial and temporal ground-truth data at a scale most academic researchers only dream of, and your work will directly define how the next generation of embodied AI agents bridge the gap between simulation and the physical world.
REQUIREMENTS:
• Deep expertise in 3D Computer Vision, Neural Rendering, multi-view geometry, and spatial modeling.
• Proven experience training and scaling large-scale 3D vision models from scratch using PyTorch and multi-terabyte datasets.
• Experience writing custom CUDA kernels to accelerate 3D operations, ray marching, or rasterization.
• Strong signals: First-author publications in top-tier venues (CVPR, ICCV, ECCV) focusing on 3D deep learning, neural rendering, or spatial transformers.
• Specific projects that include deploying 3D assets to WebGL, custom rendering engines, or optimizing splats for interactive viewing that are available publicly.
