NVIDIA is building the world’s leading AI company, and we are looking for an expert AV and GenAI Solutions Architect to help assist customers with adoption of NVIDIA's full-stack technologies as well as building and deploying solutions around Generative AI and Physical AI and other related GPU-accelerated technologies in which intelligent agents can learn, reason, and interact the world. As part of the Automotive Solutions Architecture team, we work with some of the most innovative accelerated computing platforms focused on the development and test of autonomous vehicles, in-vehicle AI assistance, and ride sharing algorithms among other things. A Solutions Architect is the first line of technical expertise between NVIDIA and customers so you will engage directly with developers, researchers, and visionary scientists at some of the most strategic customers as well as work directly with business and engineering teams on product strategy as it pertains to these customers. Join us in this exciting endeavor!
What you'll be doing:
Engage with customers to help them scope and develop solutions for building AV perception and planning models and pipelines, simulations, synthetic data generation, and software in the loop testing, AI enhanced manipulation and navigation workflows using NVIDIA's Physical AI platforms and CUDA-X libraries.
Provide hands-on technical mentorship to partners and customers on Nvidia GenAI stack. Guide customers to develope and deploy Agentic AI workflows on our platforms, quantifying the benefits of our accelerated computing software and hardware.
Partner with Sales, Engineering, Product and other Solution Architect teams to drive NVIDIA full stack adoption. Develop a deep understanding of customer workflows and requirements, lead proof-of-concepts evaluations and provide internal feedback to drive continuous product improvements.
Build collateral (notebooks, github repos, demos, etc.) applied to workflows such as AV and GenAI data curation, model training and validations, LLMs, VFMs, video encoding/decoding, etc.
What we need to see:
Master's or Ph.D. in Computer Science, Artificial Intelligence, or equivalent experience.
8+ years of hands-on experience in a technical AI role, with a strong emphasis on AV End-to-End models and GenAI model development.
Experience writing production codes in Python, or C++ and proficiency with Linux.
Hands-on experience with DevOps tools such as GitLab, Docker, and Kubernetes.
Strong understanding of AV systems (Sensors, dynamics, perception, prediction, planning, control).
Experience with DL and RL algorithms and frameworks such as PyTorch.
Enjoy working with multiple levels and teams across organizations (engineering/research, product, sales and marketing teams).
Effective verbal/written communication, and technical presentation skills.
Self-starter with a vision for growth, real passion for continuous learning and sharing findings across the team.
Ways to stand out from the crowd:
Experience with AV sensors, data curation pipelines, world models, simulations workflows and tools e.g., Carla.
Experience with Agentic AI frameworks, tools, and protocols like LangChain, LangGraph, MCP or equivalent experience.
Understand computational characteristics of Multimodal LLMs, VLMs, DiT, etc.
Experience in deploying LLM models at scale on mainstream cloud providers (e.g., AWS, Azure, GCP).
Proven track record to profile and optimize inference latency and throughput, memory and I/O utilization.
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
- Master’s or Ph.D. in Computer Science, Artificial Intelligence, or equivalent experience
- 8+ years of hands-on experience in a technical AI role
- Strong experience with autonomous vehicle end-to-end models and Generative AI model development
- Production programming experience in Python or C++
- Proficiency with Linux
- Experience with GitLab, Docker, and Kubernetes
- Strong understanding of autonomous vehicle sensors, dynamics, perception, prediction, planning, and control
- Experience with deep learning and reinforcement learning algorithms and PyTorch
- Effective verbal, written communication, and technical presentation skills
- Experience with autonomous vehicle sensors, data curation pipelines, world models, simulations, and CARLA
- Experience with Agentic AI frameworks, tools, and protocols such as LangChain, LangGraph, or MCP
- Understanding of multimodal LLMs, VLMs, and DiT computational characteristics
- Experience deploying LLM models at scale on AWS, Azure, or GCP
- Experience profiling and optimizing inference latency, throughput, memory, and I/O utilization
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 — 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.
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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.
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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.”
