Senior Solutions Architect, Autonomous Vehicles - Data Center

NVIDIA

Us, CA, United States
Base: 184,000 usd - 287,500 usd (level 4), 224,000...
Ml/dl infrastructure for autonomous vehicles
Python, cuda, or c++ programming
Linux proficiency
NVIDIA is building the world’s leading AI company focused on accelerating Physical AI workloads for Autonomous Vehicles using full-stack technologies

Job Summary

  • NVIDIA is building the world’s leading AI company focused on accelerating Physical AI workloads for Autonomous Vehicles using full-stack technologies.
  • The Solutions Architect role involves engaging directly with strategic customers and internal teams to develop and demonstrate AI solutions and optimize performance on NVIDIA GPU architectures.
  • The position offers a competitive base salary range, equity, benefits, and the opportunity to work on cutting-edge AI and robotics technologies in a diverse and inclusive environment.

Matching Summary

NVIDIA is building the world’s leading AI company focused on accelerating Physical AI workloads for Autonomous Vehicles using full-stack technologies.

Salary

Base: 184,000 USD - 287,500 USD (Level 4), 224,000 USD - 356,500 USD (Level 5); Bonus/Equity: Eligible for equity; Benefits: Eligible for benefits

Skills & Requirements

Must-have

  • ML/DL infrastructure for Autonomous Vehicles
  • Python, CUDA, or C++ programming
  • Linux proficiency
  • DevOps tools like GitLab, Docker, Kubernetes
  • NVIDIA profiling tools Nsight System/Compute
  • AV L3/L4 models and simulations

Nice-to-have

  • Technical leadership experience
  • Robotics model development
  • Deploying ML/DL models at scale on cloud GPU clusters
  • Development with NVIDIA software libraries and GPUs
  • Effective communication and technical presentations

Key Requirements

  • BSc/MSc/PhD or equivalent technical experience
  • 8+ years ML/DL infrastructure experience in Autonomous Vehicles
  • Strong understanding of AV L3/L4 models and simulations
  • Proficiency in Python, CUDA, or C++
  • Experience with DevOps tools GitLab, Docker, Kubernetes

Work Rights

Not specified

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