Simulation Researcher/Engineer

THE ROLE We're building a sufficiently faithful, controllable simulation of the world — on top of Luma's generative video and 3D models — as the substrate for training general-purpose robot policies. As a Simulation Researcher/Engineer, you'll help define what that substrate looks like. You will sit at the boundary between generative models and classical physics simulation, and you'll be one of the people who decides where each one earns its keep. WHAT YOU'LL DO - Design simulation environments that are visually rich, physically plausible, and trainable at scale — a hybrid of generative rollouts and physics-engine-based scenes. - Build the evaluation harness that tells us whether our world model is good enough to train robots on (sim-to-real gap, physical consistency, long-horizon coherence). - Develop differentiable and GPU-accelerated simulation pipelines where they unlock new training signal. - Drive the asset, scene, and task generation pipelines — including using Luma's own generative stack to bootstrap diversity. - Collaborate with world-model researchers (upstream) and policy-learning researchers (downstream). MINIMUM QUALIFICATIONS - Strong background in robotics simulation, computer graphics, physics-based modeling, or generative 3D — degree or equivalent practical record. - Fluency in Python and C++; deep familiarity with at least one production physics engine (Isaac Sim/Lab, MuJoCo, Bullet, PhysX, Drake). - Track record of building simulation systems other people actually used. PREFERRED - Research on sim-to-real transfer, domain randomization, differentiable simulation, or neural-rendering-based simulation. - Game engine, CGI, animation, or photogrammetry background. - Publications at top venues (CoRL, RSS, ICRA, NeurIPS, SIGGRAPH). Compensation The base pay range for this role is $250,000 – $450,000 per year. About Luma Luma’s mission is to build unified general intelligence that can generate, understand, and operate in the physical world. We believe that multimodality is critical for intelligence. To go beyond language models and build more aware, capable and useful systems, the next step function change will come from vision. So, we are working on training and scaling up multimodal foundation models for systems that can see and understand, show and explain, and eventually interact with our world to effect change.