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.
About the role
We're looking for a neural objects researcher to own the architecture for generating 3 D objects from video. Given egocentric footage of someone manipulating an object, you build the models that recover its geometry — and, ultimately, a simulatable asset with the physical properties to drop into a physics simulator.
You'll own the hardest, most open part of the objects effort: reconstructing objects that are small in frame, partly occluded by the hand, and sometimes deformable. You'll push the frontier, then hand results to the integrations researcher who takes them into the production pipeline.
This is a research role for someone who wants an open, unsolved problem on real, large-scale data — not a fully-specified spec.
What you'll do
Neural 3 D reconstruction. Models that recover object geometry from monocular / egocentric video — sequential and multi-view approaches robust to occlusion and low resolution.
Generative refinement. Use generative 3 D to complete and clean up partial reconstructions while rejecting hallucinated geometry.
Toward simulatable objects. Move beyond geometry toward assets that carry physical properties and rigging, so a reconstructed object can be simulated, not just rendered.
Contact-aware reconstruction. Use hand contact as a signal to constrain object shape and pose where the hand touches the object.
Deformables.



Take on the genuinely hard frontier — objects that change shape as they're handled.
Prototype & hand off. Build research prototypes and hand them to the integrations researcher for productization; publish where it makes sense.
What we're looking for
Strong 3 D / neural-reconstruction research. Neural implicit or explicit 3 D, 4 D reconstruction, or generative 3 D — you've worked at this frontier.
Deep-learning depth. You design and train models, and can read and reproduce frontier papers.
3 D geometry foundation. Structure-from-motion / multi-view geometry, meshes, and differentiable rendering.
Research taste. You can find the tractable decomposition of a hard, open problem and make steady progress on it.
Engineering to match. You prototype fast in clean Python / Py Torch and can get a research idea working on real data.
Strong plus
Generative 3 D (diffusion / feed-forward 3 D-generation families).
Differentiable rendering, Ne RF, or Gaussian splatting.
4 D / dynamic-scene reconstruction from video.
Physics simulation and rigging — turning geometry into a simulatable asset.
Hand-object interaction; publications at top vision or graphics venues.
Tech stack
Python / Py Torch (primary) for model design and training.
3 D deep learning — neural implicit / explicit representations, differentiable rendering.
Generative-3 D toolkits; mesh processing (Open3 D, trimesh).




Physics simulators for the path from geometry to simulatable object.
The exact stack matters less than depth in 3 D deep learning and the judgment to make progress on an open problem.
How this role fits — you own neural 3 D object generation: reconstructing manipulated objects from monocular egocentric video, including the hard cases — heavy occlusion, low resolution, and deformable objects — and pushing toward objects that carry the physical properties needed to be simulated. This is an architecture-level research role that sets the direction the objects team builds on.
What success looks like
Manipulated objects reconstruct from egocentric video at usable quality — including small, occluded, and deformable cases.
A credible path exists from interaction video to a simulatable 3 D object.
Your research transfers into the pipeline through the integrations seat, not just into papers.
The objects pod builds on the architecture and direction you set.
Who this role is not for
Applied-only engineers who don't want open-ended research.
Researchers who need a fully-specified problem handed to them.
Anyone without a 3 D and deep-learning foundation.
Why this role at Mecka
Define how a company turns everyday manipulated objects into simulatable 3 D assets.
Own an open, high-impact research problem on real, large-scale egocentric data.
A transparent path from research to product through a dedicated integrations seat.
Direct impact on the data that trains real robots.
We are committed to creating an inclusive and supportive candidate experience. Should you require any accommodation whatsoever during the interview process, please inform us without any h

📌 Research scientist – neural 3d objects (Toronto)
🏢 Mecka.Ai
📍 Toronto