This role pays less than
80%
of similar roles. Most pay
$185,375–$243,525
— the shaded band above.
At the midpoint, this role pays about
$177k
versus about
$214k
for comparable roles.
Based on 240 similar postings.
Employer
About Microsoft
Microsoft Corporation is a global technology leader producing software, hardware, and cloud services including Windows, Office 365, Azure cloud platform, Xbox gaming, and Surface devices. Industry: Software & Cloud Computing
Microsoft currently has
598 open roles
on FindRole.
Listed pay typically runs
$119,800–$234,700
across 586 roles with salary data.
As a Senior Product Manager - AI Infrastructure on the Azure HPC & AI Infrastructure team, you will own the product strategy and execution for back-end networking across GPU infrastructure. You will translate complex training and inference workload needs into specific requirements for bandwidth, latency, topology, congestion management, resiliency, security, observability, and serviceability. Your daily work involves managing the end-to-end product lifecycle from concept through qualification, launch, and fleet deployment while aligning hardware, software engineering, datacenter infrastructure, supply chain, and finance teams. You will influence supplier roadmaps for network interface devices, switches, optics, cables, and interconnect technologies like Ethernet, InfiniBand, RDMA, and NVLink. This role solves critical technical challenges in building the next generation of AI Factories by improving performance, reliability, and total cost of ownership for large-scale AI training and inference systems.
What does a Product Manager earn?
Median $204200 from 424 postings across 77 companies.
AI
HPC
Data Center Infrastructure
System Architecture
Firmware
Drivers
Software Stack
ROCm
Benchmarking
Systems Engineering
Product Management
Technical Product Management
AI Inference
TensorRT-LLM
vLLM
SGLang
NVIDIA Dynamo
Triton Inference Server
Quantization
Speculative Decoding
KV Caching
LLM
Deep Learning
Generative AI
Benchmarking
Capacity Planning
Autoscaling