Simulation Researcher/Engineer

What You'll Own

• Design simulation environments that are visually rich, physically plausible, and trainable at scale — a hybrid of generative rollouts and physics-engine scenes.

• Build the evaluation harness that shows whether the 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.

First 90 Days

One way the first 90 could unfold.

• Days 1–30 — Immerse & Diagnose: Learn the generative stack and where physics simulation and generative rollouts each fit.

• Days 30–60 — Ship & Validate: Build a first hybrid simulation environment and an evaluation harness for it.

• Days 60–90 — Scale & Systemize: Scale the asset, scene, and task pipelines and make the eval a reliable training signal.

What You Bring

• A strong background in robotics simulation, computer graphics, physics-based modeling, or generative 3D, by degree or practical record.

• Fluency in Python and C++, and deep familiarity with at least one production physics engine (Isaac Sim/Lab, MuJoCo, Bullet, PhysX, Drake).

• A track record building simulation systems other people actually used.

Nice to Have

• Research on sim-to-real transfer, domain randomization, differentiable simulation, or neural-rendering-based simulation.

• A game engine, CGI, animation, or photogrammetry background.

• Publications at top venues (CoRL, RSS, ICRA, NeurIPS, SIGGRAPH).

About Luma: Luma's mission is to build unified general intelligence that can generate, understand, and operate in the physical world. We believe multimodality is critical for intelligence — the next step beyond language models comes from vision. Luma is an equal opportunity employer.