Senior Solutions Architect, Autonomous Driving - Genai

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Senior Solutions Architect focused on Generative AI and Autonomous Vehicles, engaging with customers to guide adoption of NVIDIA's full-stack technologies, including AI platforms, CUDA-X libraries, and GenAI/Physical AI solutions. Responsibilities include technical mentorship, developing AV perception and planning models, simulations, synthetic data generation, AI-enhanced manipulation/navigation, and building collateral for AI workflows. Requires strong experience in AV systems, GenAI model development, Python/C++, Linux, DevOps, and DL/RL frameworks.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. 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.

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 emphasis on AV End-to-End models and GenAI model development
  • production codes in Python, or C++
  • proficiency with Linux
  • DevOps tools such as GitLab, Docker, and Kubernetes
  • Strong understanding of AV systems (Sensors, dynamics, perception, prediction, planning, control)
  • DL and RL algorithms and frameworks such as PyTorch

Nice to have

  • 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.

What the JD emphasized

  • 8+ years of hands-on experience in a technical AI role
  • strong emphasis on AV End-to-End models and GenAI model development
  • Experience with Agentic AI frameworks, tools, and protocols like LangChain, LangGraph, MCP or equivalent experience

Other signals

  • customer engagement
  • technical mentorship
  • product strategy
  • GenAI
  • Autonomous Vehicles