Senior Asset Pipeline Engineer, Sim-Ready Content

Description

About Genesis AI

Genesis AI builds the simulation platform powering the next generation of robotics and embodied AI. Our flagship product, Genesis World, is a unified multi-physics simulation engine paired with Nyx, our in-house GPU-accelerated, path-traced photorealistic renderer. Together they provide a platform for training robotics and embodied AI at scale; letting teams simulate complex physical worlds with high fidelity and speed, from a laptop to datacenter-grade GPUs, and close the gap between simulation and the real world.

About the Role

A simulator is only as useful as the content that runs inside it. Training embodied agents at scale means large volumes of assets that are simultaneously physically valid and visually convincing and that is a pipeline problem before it is an art problem.

We're hiring a Senior Engineer to build the path from raw input to sim-ready asset: geometry from a range of sources going in; clean meshes, correct collision proxies, plausible physical properties, materials that hold up under a path tracer, and reliable metadata coming out.

The bar is throughput and repeatability. An asset that needs a human pass to be usable doesn't scale, so the pipeline has to make good decisions on its own and flag the cases where it can't.

Part of that autonomy comes from agents embedded in the graph itself. Nodes can call models to produce input content, drive generation, pick parameters, interpret a mesh before deciding how to treat it, or judge whether a result is acceptable. Making that reliable is its own engineering problem: agents are non-deterministic, they fail in ways a mesh operator doesn't, and they need guardrails, structured outputs, retries and verification around them. You'll be building that as a first-class part of the pipeline rather than bolting it on.

You'll work closely with the stakeholders, who set direction and priorities. You own the technical design and the implementation and you're expected to bring enough pipeline experience to challenge the plan where it needs challenging.

Responsibilities

• Build and maintain the end-to-end asset pipeline, from ingestion through to validated output
• Design agent-driven nodes that generate input content and make decisions inside the graph, with the structured outputs, guardrails and fallbacks that make them dependable in batch
• Develop automated processing stages: mesh repair, retopology, decimation and LODs, UV unwrapping and texel density normalization, collision proxy generation
• Derive physical properties so assets behave correctly in simulation: mass, inertia, contact parameters, articulation setup
• Handle appearance: material conversion to PBR, texture processing, baking, and color management so assets render correctly in Nyx
• Define and enforce asset standards (units, orientation, pivots, naming, provenance) and build the validators that reject noncompliant assets before they reach the library
• Scale the pipeline to large batch runs, with caching, versioning and selective reprocessing when a stage changes
• Build quality checks into the pipeline itself: physics stability harnesses, render smoke tests, automated evaluation of generated content, comparison on reprocessed assets
• Work with the rendering, physics and research teams on what sim-ready needs to mean for each downstream use

Requirements

• 5+ years building content or asset pipelines in games, VFX, simulation, or 3D tooling, with ownership of a pipeline in production
• Deep knowledge of 3D asset formats and their quirks: USD, glTF, FBX, OBJ, plus physics description formats such as URDF or MJCF
• Strong Python and C++ where processing needs to be fast
• Hands-on mesh processing expertise: remeshing, decimation, UV generation, repair operations, convex decomposition, and a clear sense of how each one fails
• Solid command of PBR material authoring, texture baking and color spaces
• Experience driving DCC tools headlessly (Blender, Houdini, Maya) and building batch or distributed job systems around them
• Practical experience putting LLM or agent calls into production systems: prompting, tool use, structured output, cost and latency control, and handling non-determinism
• Understanding of what makes an asset physically valid in a simulator: visual versus collision geometry, mass properties, contact behavior, numerical stability
• Judgment about quality bars, including when an asset is good enough and when it isn't
• Experience processing output from generative or reconstruction-based 3D sources, and evaluating that output automatically
• Agent frameworks, orchestration and eval tooling applied to content generation
• OpenUSD at scale: composition, layering, MaterialX
• Semantic annotation and dataset curation at volume
• Exposure to simulation stacks such as MuJoCo, Isaac Sim or similar