Simulation Engineer

Noble Machines, Inc•Sunnyvale, CA

8d•$170,000 - $400,000•Onsite

About The Position

Noble Machines builds multipurpose robots to support human workers in demanding industrial jobs, including construction, mining, and energy. The company focuses on delivering rugged, reliable products for real-world applications. The Simulation Engineer builds accurate, efficient digital twins of robots, objects, and industrial environments for robot learning, data generation, and policy evaluation. The role owns simulation assets, environment performance, and measurement of the sim-to-real gap in physical interaction and perception. The position is based onsite in Sunnyvale, California.

Requirements

Experience building digital twins, simulation assets, or physically interactive environments for robotics, industrial simulation, or comparable applications.
Practical understanding of collision detection and contact modeling, including how collision geometry, physical parameters, and solver settings affect behavior, stability, and performance.
Experience optimizing 3D assets and scenes for runtime use, including mesh complexity, materials, textures, rendering cost, and memory consumption.
Hands-on experience with Isaac Sim, Omniverse, MuJoCo, or a comparable physics-based simulation platform.
Familiarity with USD and robot descriptions such as URDF, or equivalent formats and workflows.
Solid understanding of 3D geometry, coordinate transforms, rigid-body dynamics, and articulated systems, with the ability to diagnose incorrect geometry, inertial properties, joint configuration, and contact behavior.
Working understanding of how simulation supports robot learning and evaluation, and judgment to choose the visual and physical fidelity appropriate to a task.

Nice To Haves

Experience with contact-rich manipulation, grasping, articulated mechanisms, or robotics simulation.
Experience with Isaac Lab, PhysX, scene generation, or synthetic data pipelines with configurable cameras and annotations.
Experience with CAD conversion, photogrammetry, scene reconstruction, or asset processing using Blender, Houdini, or similar tools.
Experience using simulation for imitation learning or reinforcement learning, including randomized environments and parallel rollouts.

Responsibilities

Build simulation-ready robot parts, tools, and scenes from CAD, descriptions, and scanned or reconstructed scenes, creating reusable assets with consistent scale, coordinate frames, and structure.
Develop collision representations that preserve task-critical geometry and contact behavior, tuning mesh simplification, convex decomposition, and primitive approximations to balance accuracy, stability, and computational cost.
Measure the physical sim-to-real gap by designing matched hardware and simulation tests, quantifying differences in motion and contact behavior, and calibrating mass, inertia, friction, and collision models.
Work with hardware and robotics engineers to collect reference measurements and track improvements to physical simulation fidelity.
Measure the perception sim-to-real gap by comparing rendered and real camera observations under matched viewpoints and conditions, and work with learning and evaluation engineers to quantify effects on perception or policy performance.
Use perception-gap findings to guide visual meshes, materials, textures, lighting, and camera configuration.
Profile physics and rendering separately and optimize geometry and texture budgets, asset reuse, instancing, memory use, scene loading, and simulation throughput while maintaining numerical stability and task-relevant detail.
Develop programmable task environments, scene variations, randomization, and reset logic for training, data generation, and evaluation, integrating them with learning and evaluation pipelines.
Automate asset import, conversion, and validation; review and improve externally supplied assets; and maintain reproducible scenes that teammates can use and extend.