Description
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.
Responsibilities
- 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.
Requirements
- 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.
Nice to Have
- 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.