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NVIDIA

Senior Solutions Architect, Autonomous Driving - GenAI

NVIDIA is hiring a Senior Solutions Architect, Autonomous Driving - GenAI in Santa Clara, United States. It pays $184k-$288k a year and Level rates it ; you can apply on NVIDIA.

Level's read on this job

  • On Level's scale, this solutions architect role is Builds AI ●●●●: the job is building AI systems.
  • The listed pay starts 9% above the typical solutions architect posting on Level.
  • Tools and skills the posting cites include LangChain, LangGraph and PyTorch.
  • Level classifies it as a senior role.
  • Level lists 2,700+ other open roles at NVIDIA.

AI in this role

LangChainLangGraphPyTorch

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.

Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary range for this position is 184,000 USD – 287,500 USD.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until November 3, 2026.

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.

How we rate this

Senior Solutions Architect, Autonomous Driving - GenAI at NVIDIA rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

LangChainLangGraphPyTorch

Also common in solutions architect jobs at this level

Pay

This job starts at
$184k
Typical solutions architect job
$169k

This posting starts 9% above the typical solutions architect job. See solutions architect jobs

Questions you could be asked

  1. What's a project where you used LangChain hands-on?
  2. Walk me through how you've used LangGraph in your day-to-day work.
  3. What are the limits of PyTorch that you've run into, and how did you work around them?
  4. How would you decide a model or AI system is ready to ship?
  5. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: LangChain, LangGraph, and PyTorch. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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