Physical AI is redefining what robots can do, and embedded platforms are where that future becomes real! We are looking for a hands-on Solutions Architect with deep embedded systems expertise to serve as a technical anchor for NVIDIA’s Physical AI ecosystem. This role sits at the intersection of hardware bring-up, sensor integration, and deployment of next-generation robotics models. You will bridge pioneering research and real-world application engineering, working closely with customers and NVIDIA Engineering, Product, Sales, and Ecosystem teams to turn prototypes into production-grade, AI-accelerated robotics systems.
Are you are passionate about Robotics and ready to make a meaningful difference? If so, this role fits you!
What you'll be doing:
Serve as the primary embedded systems expert for NVIDIA Physical AI partners using technologies such as Jetson, Holoscan, Isaac ROS, Isaac OS, GR00T.
Engage with customers to ensure smooth integration of production stacks, including real-time Linux environments, ROS 2, sensor pipelines, and edge AI models.
Deploy, profile, and optimize robotics foundation models (VLAs, World Models) on embedded computers and guide customers on tradeoffs
Translate customer requirements into practical NVIDIA-based architectures, and work closely with internal teams to feed field insights into product feedback and roadmap priorities.
Lead technical discussions, presentations, and hands-on workshops with key partners, while developing proof-of-concepts, and reference implementations.
What we need to see:
BS in Electrical Engineering, Computer Engineering, Computer Science, Robotics, Mechanical Engineering, or a related field (or equivalent experience).
5+ years of hands-on experience in embedded systems, including developing on NVIDIA Jetson, performance tuning, and system-level debugging.
Strong fluency in ROS 2 and background in deploying robotics autonomy stacks, and sim-to-real validation workflows.
Previous work integrating sensor pipelines such as cameras, LiDAR, and IMU
Proven expertise in AI model deployment, optimization, and GPU profiling on edge hardware, using SDKs like TensorRT
Outstanding communication and collaboration skills, with the ability to translate complex technical concepts for researchers, engineers, and business teams.
Ways to stand out from the crowd:
Hands-on experience with NVIDIA Robotics libraries such as Isaac ROS and cuVSLAM, as well as simulation frameworks like Isaac Sim and Isaac Lab
Familiarity with deploying and optimizing VLMs, VLAs (such as GR00T) or World Models (such as Cosmos) on edge platforms, including techniques like quantization, compression, and distillation.
Experience with camera and sensor software stacks such as NVIDIA’s HSB (Holoscan Sensor Bridge), V4L2, GMSL cameras, ISP tuning, or high-throughput video processing.
Prior experience with real-time and safety-aware embedded robotics systems in industries like autonomous vehicles or manufacturing.
Experience using agentic tooling to accelerate integration, debug, and build reference-implementation work.
152,000 USD – 241,500 USD for Level 3; and 184,000 USD – 287,500 USD for Level 4.
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
- BS in Electrical/Computer/Robotics/Mechanical Engineering, Computer Science, or equivalent experience
- 5+ years hands-on embedded systems experience including development on NVIDIA Jetson, performance tuning, and system-level debugging
- Experience with real-time Linux environments and deploying production stacks
- Strong fluency in ROS 2 and experience deploying robotics autonomy stacks and sim-to-real validation workflows
- Experience integrating sensor pipelines (cameras, LiDAR, IMU)
- Expertise in AI model deployment, optimization, and GPU profiling on edge hardware using SDKs like TensorRT
- Outstanding communication and collaboration skills
- Hands-on experience with NVIDIA Robotics libraries (Isaac ROS, cuVSLAM) and simulation (Isaac Sim, Isaac Lab)
- Familiarity deploying and optimizing VLMs/VLAs/World Models on edge platforms and techniques like quantization, compression, distillation
- Experience with camera/sensor stacks (HSB, V4L2, GMSL), ISP tuning, or high-throughput video processing
- Experience with real-time and safety-aware embedded robotics systems in industries like AV or manufacturing
- Experience using agentic tooling to accelerate integration and debugging
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.
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Equity Value & Accessibility — Company stock and RSUs have driven exceptional total compensation, with a generous employee stock purchase plan and ongoing equity refreshers playing a central role. Feedback suggests historic share-price gains created significant financial upside for many.
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Healthcare Strength — Multiple plan options (HSA, PPO, and in some locations Kaiser) with in-network preventive care covered at 100% and company HSA contributions indicate a robust offering. Mental-health sessions and broad dental, vision, and disability coverage further strengthen the package.
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Parental & Family Support — Up to 22 weeks of fully paid parental leave, adoption and surrogacy reimbursement, and coverage for IVF and egg freezing are highlighted as standout features. Additional family-care resources and caregiving support broaden the appeal.
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.”
