NVIDIA
Global Account Manager, Developer Relations - Google
$224K–$431K
US, Remote · Manager · Full-time · Posted Sep 28, 2026
- Experience
- 12+ years of overall professional experience in the technology industry in specified fields, including 3+ years of hands-on experience applying accelerated computing to production workloads.
- Education
- Bachelor's or Master's degree in Computer Science, Engineering, Electrical Engineering, or a similar field (or equivalent experience).
About the job
Leads technical partnership and developer relations with Google and Alphabet teams to integrate NVIDIA software into their platforms and deliver joint solutions.
What you'll do
- Drive adoption and integration of NVIDIA libraries, models, and frameworks across partner products and platforms.
- Coordinate cross-functional technical teams, resolve integration challenges, and influence partner roadmaps and NVIDIA product strategy.
- Build executive and developer relationships and collaborate with go-to-market teams on joint solutions and wins.
What they ask for
- Bachelor's or Master's degree in Computer Science, Engineering, Electrical Engineering, or a similar field (or equivalent experience).
- Deep understanding of hyperscaler platform ecosystems and their software stacks.
- Ability to structure complex technical engagements, negotiate requirements, and collaborate across internal and external teams.
- Experience influencing partner product roadmaps and communicating technical concepts to audiences from developers to C-suite executives.
Nice to have
- Hands-on experience with NVIDIA software libraries, frameworks, and models; familiarity with JAX and XLA training stacks at scale.
- Experience engaging AI Research organizations or working at or alongside Alphabet.
Pay
224,000 USD - 356,500 USD base salary; 272,000 USD - 431,250 USD base salary
Tools
CUDA-X, NVIDIA Agent Toolkit, CUTLASS, CuTeDSL, cuDNN, NCCL, JAX, XLA
AI focus
Integrate NVIDIA libraries, open models, and frameworks into partner platforms; support accelerated AI/ML and agentic workloads, inference serving and optimization, distributed training, and model optimization.
Model-written summary · the original posting is authoritative