The Nori robot, mid-wave
Simulation · Neural Rendering2026

Nori Robotics

Founding engineer for systems and graphics at Nori Robotics (YC S26).

As a founding engineer I worked on the real-time C++ engine that drives the robot, working with ROS2 and our custom packages that surround it, such as the camera pipeline that streams four live views off a rpi5. Most of the work was optimizing on such limited on-board compute, eg rebuilding the frame path around an event-driven ROS2 to optimize the robot's CPU use, dealing reliably with latency in cloud compute, etc.

On the graphics side, a synthetic data pipeline in IsaacSim relights and re-textures captured scenes, so the robot's learned policies see far more of the world than we could ever film. There is also a browser-based simulator in three.js for posing and driving the robot from a tab, with SSAO and temporal antialiasing so it still reads like a product render.

I also own the Nori SDK end to end, from architecture to what ships on PyPI.

I'm also responsible for provisioning design, over the air updates, and getting our first deployments in the field.

The 3D renders across the company site and marketing are my work. This includes modeling, lighting, full animations, one consistent design language. Mostly done on lunch breaks. Not the point of my work here, but somebody had to make the robot look good.

Browser simulator ↗Nori SDK on PyPI ↗GitHub ↗
Relighting pipeline: a captured frame decomposed into base color, normal, depth, roughness and metallic buffers, then recomposited under new lighting.
Relighting pipeline: a captured frame decomposed into base color, normal, depth, roughness and metallic buffers, then recomposited under new lighting.
One captured demo relit into five lighting banks by three mechanisms (TC-Light, DiffusionRenderer and UniRelight) for cross-environment training data.
One captured demo relit into five lighting banks by three mechanisms (TC-Light, DiffusionRenderer and UniRelight) for cross-environment training data.
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