Senior Solutions Architect, Robotics Simulation

This is a fully remote position, open to applicants in California.

📋 Description

• Collaborate with robotics partners to implement, enhance, and expand NVIDIA’s simulation technologies.

• Develop and refine workflows that encompass robot rigging, rigid and deformable-body physics, multi-solver integration, rendering, sensor simulation, and synthetic data generation.

• Identify and address integration and performance challenges across simulation, rendering, data movement, accelerated computing, and large-scale workloads.

• Oversee technical assessments, proofs of concept, workshops, and reference implementations.

• Test innovative methods with partners, including hybrid physics-based and neural-model techniques, and incorporate them into learned-policy workflows and sim-to-real deployment.

• Work alongside NVIDIA Research, Engineering, Product, business teams, and clients to convert ecosystem requirements into product insights, roadmap priorities, and reusable guidelines.

⛳️ Requirements

• BS, MS, PhD, or equivalent experience in Robotics, Computer Science, Mechanical Engineering, Electrical Engineering, or a related discipline.

• A minimum of 5 years of practical experience in building robotics simulation applications.

• Extensive knowledge of robotics simulation platforms such as Isaac Sim, MuJoCo, Gazebo, or Drake.

• Proficiency in physics, large-scale rendering, sensor modeling, or robot rigging.

• Familiarity with ROS 2 and robotics description formats such as OpenUSD, URDF, or MJCF.

• Strong programming skills in Python or C++.

• Experience with API integration and distributed software systems.

• Solid understanding of GPU-accelerated computing and its relevance to robotics simulation and machine learning workflows.

• Excellent communication and teamwork abilities.

• Practical experience with NVIDIA simulation technologies such as Isaac Sim, Newton, PhysX, RTX, or NVIDIA Warp.

• Previous involvement with Isaac Lab, large-scale reinforcement learning, data generation, or video and data reconstruction pipelines.

• Experience in industrial manipulation tasks including grasping, perception-guided motion planning, force and impedance control, and learned methods like RL and IL.

• Familiarity with integrating virtual controllers and simulations involving contact-rich manipulation.

• Knowledge of simulation-to-real techniques, including system identification, domain randomization, calibration, and validation on actual hardware.

• Proven history of collaborating with robotics companies, researchers, or developers to implement new technologies in production.

🏝️ Benefits

• Equity

• Benefits