Job Description
We're looking for a Simulation Engineer to build and own the simulation pipeline that powers AI training for our robots. You will create realistic 3D environments, place and configure our robot platforms inside them, and deliver high-quality synthetic data and evaluation scenarios that downstream learning teams can rely on. Your work sits on the critical path between product hardware, autonomy, and model training: the environments you build need to be physically plausible, repeatable, and scalable enough to train policies that transfer to the real world. What you'll do Design, implement, and maintain an end-to-end simulation pipeline for robot AI training (environment authoring → asset ingestion → robot placement → physics/sensors → data export) Build and iterate realistic 3D scenes that reflect real deployment contexts (layouts, objects, materials, lighting, clutter, and variation) Import, configure, and validate robot models, end-effectors, sensors, and calibration in simulation so they match hardware behavior closely enough for learning Define standards for scene composition, asset versioning, randomization, and reproducibility across training runs Generate and curate synthetic datasets (RGB-D, segmentation, proprioception, contact, and related modalities) for policy and VLA training workflows Partner with RL, perception, and deployment engineers to close the sim-to-real gap: identify failure modes, tune domain randomization, and improve evaluation suites Instrument simulations for benchmarking, regression testing, and continuous integration as the robot fleet and models evolve Document pipelines, scene libraries, and operational runbooks so others can extend and run simulations reliably
