NVIDIA's AI Software Platforms team is hiring a senior technical product manager to drive the development of cutting-edge libraries and frameworks that enhance GPU-based inference deployments. This role involves architecting developer-focused products that simplify high-performance deployment across diverse GPU architectures, defining long-term strategies for kernel and communication libraries by analyzing performance bottlenecks in emerging workloads, collaborating with CUDA engineers to design intuitive abstractions for memory and distributed execution, and partnering with open-source communities like Triton and FlashInfer to shape ecosystem-wide roadmaps. Ideal candidates have over seven years of experience as a technical product manager in GPU acceleration, deep expertise in CUDA execution models and multi-GPU protocols, and a strong track record of translating hardware capabilities into software strategies. They should also possess advanced knowledge in parallel computing architectures, contribute to performance-critical open-source projects, craft GitHub-first developer tools with significant community engagement, and have experience building cost-per-inference models that factor in energy efficiency and cluster scaling.
Skills
CUDA
C++
Python
Triton
FlashInfer
GitHub
HPC
GPU
Docker
Kubernetes
CI/CD
PostgreSQL
GitLab
Prometheus
Grafana
What you'll do
Architect products that simplify high-performance inference deployment across diverse GPU architectures.
Define multi-year strategy for kernel and communication libraries by analyzing AI workload bottlenecks.
Design intuitive abstractions for memory and distributed execution with CUDA kernel engineers.
Partner with open-source communities to shape ecosystem-wide roadmaps for Triton, FlashInfer.
Craft GitHub-first developer tools with significant community engagement metrics.
What we're looking for
7+ years of technical PM experience in shipping developer products for GPU acceleration.
Expert-level understanding of CUDA execution models and multi-GPU protocols.
BS or MS in Computer Engineering or equivalent expertise in parallel computing architectures.
Experience contributing to performance-critical open-source projects with significant adoption impact.
Strong track record of crafting GitHub-first developer tools with high community engagement.
Published research on GPU kernel optimization, collective communication algorithms, or ML model serving.
Experience building cost-per-inference models considering hardware utilization and energy efficiency.
Market check
Salary context
This
$168,000–$258,750
range sits above
57%
of similar postings on FindRole.
Peer median band
$167,700–$241,500
Median floor and ceiling across peers.
Typical midpoint (25–75%)
$159,750–$245,000
Middle half of comparable postings.
Based on 240 comparable postings.*
* 240 is the maximum number of comparable postings sampled.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
802 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 798 roles with salary data.
San Jose, California, US33 days ago
$211,600–$286,200
AI
ML
Datacenter
Cloud
Product Management
Systems Thinking
Competitive Analysis
GenAI
ML Accelerators
Hardware
Software
Infrastructure
Performance Analysis
System Layers
End-to-End System Behavior
Problem Solving
Technical Analysis
Customer Workloads
Product Roadmap
Engineering Priorities