Senior Solutions Architect, Embedded Physical AI
NVIDIA · Remote · Santa Clara, United States · 2mo ago
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.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
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