Job description

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. THE ROLE We are hiring a Research Scientist to architect and train proprietary 3D reconstruction pipelines from scratch. You will transform our continuous stream of high-quality, real-world multi-modal data into highly accurate, photorealistic digital environments using techniques such as 3D Gaussian Splatting and NeRFs. Beyond building the core architectures, you will integrate these reconstructed environments to improve other segments of our perception pipeline and optimize these spatial assets for seamless client delivery and interactive viewing. WHAT YOU'LL WORK ON - 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 our 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. WHAT YOU BRING - 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. WHY THIS ROLE? - The Data Advantage: Access proprietary spatial and temporal ground-truth data at a scale that most academic researchers only dream of. - High Impact: Your spatial models and environmental reconstructions will directly define how the next generation of embodied AI agents bridge the gap between simulation and the physical world. 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.

Requirements

Must-haves

We are hiring a Research Scientist to architect and train proprietary 3D reconstruction pipelines from scratch.
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.
Emergent Perception R&D: Rapidly prototype new architectures to isolate dynamic actors from static environments, resolve algorithmic bottlenecks, and fuse novel hardware sensor integrations.
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.
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.
WHY THIS ROLE?
The Data Advantage: Access proprietary spatial and temporal ground-truth data at a scale that most academic researchers only dream of.
High Impact: Your spatial models and environmental reconstructions will directly define how the next generation of embodied AI agents bridge the gap between simulation and the physical world.
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.

Nice-to-haves

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Use the role’s language and application details while tailoring your materials.

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Platform: ashby
Effort: quick

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