This role pays less than
74%
of similar roles. Most pay
$177,250–$246,150
— the shaded band above.
At the midpoint, this role pays about
$177k
versus about
$212k
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
622 open roles
on FindRole.
Listed pay typically runs
$119,800–$234,700
across 559 roles with salary data.
As a Senior AI Hardware Architect at Microsoft’s AHSI team, you will join the AI Systems Architecture (ASA) group to drive analytical performance modeling and workload characterization on GPU and in-house accelerators. Your daily tasks include identifying performance bottlenecks, developing models for new architectural features, and correlating silicon data with software traces to optimize performance, efficiency, and cost. You’ll collaborate closely with architecture, microarchitecture, compiler, runtime, networking, and systems teams to develop tools that enhance debugging and decision-making processes. With expertise in GPU and AI accelerator architectures, analytical modeling, and Python/C++ programming, you will contribute to the shaping of future AI accelerator and system architectures, addressing challenges in large-scale cloud infrastructure for Microsoft’s expansive suite of online services.
Lead performance analysis and modeling of AI workloads on GPUs and accelerators.
Identify and optimize bottlenecks in memory systems, communication collectives, and workload mappings.
Develop models to evaluate new architectural features and innovations for perf/W optimization.
Correlate silicon measurements with software traces to improve model fidelity and guide architecture decisions.
Drive performance optimizations at kernel, runtime, and system levels for AI training and inference.
What we're looking for
Master's Degree in a relevant field and 3+ years of technical engineering experience or equivalent.
Strong understanding of GPU and AI accelerator architectures including compute pipelines, memory hierarchies, interconnects, collective communication, and parallel execution models.
Experience with analytical performance modeling, architectural simulation, workload characterization, and silicon correlation for accelerator and system design.
Expertise in performance profiling, benchmarking, and root-cause analysis using hardware counters, software traces, and workload-level measurements.
Hands-on experience analyzing and optimizing AI kernels, connecting kernel behavior to architectural and system-level performance.