We are building a team of innovators helping robotics partners develop and adopt the next generation of Physical AI, spanning simulation, data generation, model training, and deployment!
We are looking for a hands-on Solutions Architect with strong applied engineering expertise in robotics simulation, manipulation, and data generation. You will work closely with robotics researchers and developers to translate emerging research into real-world robotics solutions, building and scaling simulation and sim-to-real workflows. Collaboration spans NVIDIA Research, Engineering, Product, and customers, helping shape both our robotics platforms and customer's real-world adoption. Come join us and help accelerate the future of Physical AI!
What You’ll Be Doing:
Work with robotics partners to adopt, extend, and scale NVIDIA’s simulation technologies.
Build and optimize workflows spanning robot rigging, rigid and deformable-body physics, multi-solver integration, rendering, sensor simulation, and synthetic data generation.
Diagnose integration and performance bottlenecks across simulation, rendering, data movement, accelerated computing, and large-scale workloads.
Lead technical evaluations, proofs of concept, workshops, and reference implementations that accelerate platform adoption.
Validate emerging approaches with partners, including hybrid methods combining physics-based simulation with neural models, and apply them to learned-policy workflows and sim-to-real deployment.
Collaborate with NVIDIA Engineering, Product and business teams to translate ecosystem needs into product feedback, roadmap priorities, and reusable guidelines.
What We Need to See:
BS, MS, PhD, or equivalent experience in Robotics, Computer Science, Mechanical Engineering, Electrical Engineering, or a related field.
5+ years of hands-on experience building robotics simulation applications.
Deep expertise with robotics simulation platforms like Isaac Sim, MuJoCo, Gazebo, or Drake, and core simulation domains such as physics, large-scale rendering, sensor modeling, or robot rigging.
Familiarity with ROS 2 and robotics description formats such as OpenUSD, URDF, or MJCF.
Proficiency in Python or C++, with experience integrating APIs and distributed software systems
Understanding of GPU-accelerated computing, and how to apply it to robotics simulation, and machine learning workflows.
Excellent communication and collaboration skills, with the ability to tackle complex problems alongside diverse audiences.
Ways to Stand Out From the Crowd:
Hands-on knowledge of NVIDIA simulation technologies such as Isaac Sim, Newton, PhysX, RTX or NVIDIA Warp.
Previous work with Isaac Lab, large-scale reinforcement learning, data generation, or video and data reconstruction pipelines.
Industrial manipulation background, spanning grasping, perception-guided motion planning, force and impedance control, and learned approaches such as RL, IL, as well as experience integrating virtual controllers and contact-rich manipulation simulations.
Familiarity with simulation-to-real techniques, including system identification, domain randomization, calibration, and validation on real hardware.
Track record of working with robotics companies, researchers, or developers to bring new technologies into production.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- Bachelor's, master's, PhD, or equivalent experience in Robotics, Computer Science, Mechanical Engineering, Electrical Engineering, or a related field
- 5+ years of hands-on experience building robotics simulation applications
- Deep expertise with robotics simulation platforms such as Isaac Sim, MuJoCo, Gazebo, or Drake
- Expertise in simulation domains such as physics, large-scale rendering, sensor modeling, or robot rigging
- Familiarity with ROS 2 and robotics description formats such as OpenUSD, URDF, or MJCF
- Proficiency in Python or C++
- Experience integrating APIs and distributed software systems
- Understanding of GPU-accelerated computing for robotics simulation and machine learning workflows
- Excellent communication and collaboration skills
- Hands-on knowledge of NVIDIA simulation technologies such as Isaac Sim, Newton, PhysX, RTX, or NVIDIA Warp
- Experience with Isaac Lab, large-scale reinforcement learning, data generation, or video and data reconstruction pipelines
- Industrial manipulation experience involving grasping, perception-guided motion planning, force and impedance control, reinforcement learning, imitation learning, or contact-rich simulations
- Familiarity with simulation-to-real techniques including system identification, domain randomization, calibration, and real-hardware validation
- Track record of working with robotics companies, researchers, or developers to bring technologies into production
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
-
Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
-
Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
-
Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
.png)
.png)
