This role pays less than
59%
of similar roles. Most pay
$172,000–$239,825
— the shaded band above.
At the midpoint, this role pays about
$197k
versus about
$206k
for comparable roles.
Based on 239 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
896 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 876 roles with salary data.
As a Senior Solutions Architect, Embedded Physical AI, you will serve as a technical anchor for the Physical AI ecosystem, bridging pioneering research and real-world application engineering. You will act as the primary embedded systems expert for partners, focusing on hardware bring-up, sensor integration, and the deployment of next-generation robotics models. Your daily responsibilities include translating customer requirements into NVIDIA-based architectures, leading technical workshops, developing proof-of-concepts, and optimizing robotics foundation models like VLAs and World Models. You will work with technologies including Jetson, Holoscan, Isaac ROS, Isaac OS, GR00T, TensorRT, and ROS 2. The role involves managing real-time Linux environments, sensor pipelines involving cameras, LiDAR, and IMU, and performing GPU profiling on edge hardware to solve complex problems in autonomous vehicles and manufacturing.
What does a Solutions Architect earn in Remote?
Median $231100 from 71 postings across 13 companies.
Serve as the primary embedded systems expert for partners using Jetson, Holoscan, Isaac ROS, and GR00T technologies.
Integrate production stacks including real-time Linux environments, ROS 2, sensor pipelines, and edge AI models.
Deploy, profile, and optimize robotics foundation models like VLAs and World Models on embedded computers.
Translate customer requirements into practical NVIDIA-based architectures for production-grade systems.
Provide technical guidance to customers regarding hardware trade-offs and system optimizations.
Lead technical discussions, presentations, and hands-on workshops with key partners.
Develop proof-of-concepts and reference implementations to demonstrate product capabilities.
Feed field insights from customer interactions into internal product feedback and roadmap priorities.
What we're looking for
Bachelor's degree 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 development on NVIDIA Jetson, performance tuning, and system-level debugging.
Strong fluency in ROS 2 and experience deploying robotics autonomy stacks and sim-to-real validation workflows.
Proven experience integrating sensor pipelines such as cameras, LiDAR, and IMU.
Expertise in AI model deployment, optimization, and GPU profiling on edge hardware using SDKs like TensorRT.
Experience with NVIDIA Robotics libraries (Isaac ROS, cuVSLAM) and simulation frameworks (Isaac Sim, Isaac Lab).
Familiarity with deploying and optimizing VLMs, VLAs, or World Models on edge platforms using quantization, compression, and distillation.
Experience with camera and sensor software stacks such as Holoscan Sensor Bridge, V4L2, GMSL cameras, ISP tuning, or high-throughput video processing.
AI
Machine Learning
Deep Learning
Robotics
GPU
HPC
Docker
Kubernetes
PyTorch
TensorFlow
Vision Transformer
VLM
VLA
Nsight Systems
Synthetic Data Generation
Digital Twins
AI
Machine Learning
Deep Learning
Robotics
GPU
HPC
Docker
Kubernetes
PyTorch
TensorFlow
Isaac Sim
Isaac Lab
Nsight Systems
Transformers
VLM
VLA
Synthetic Data Generation
Digital Twins
Parallel Computing