Senior Performance Modeling Engineer

Microsoft • Redmond, WA

5h • $119,800 - $261,000 • Hybrid

About The Position

Microsoft is seeking a Senior AI Performance Modeling Engineer for the AI Systems Architecture group within the Systems Planning and Architecture team in Azure Hardware Systems and Infrastructure. The role helps define and optimize next-generation AI accelerators and large-scale AI systems by developing analytical performance models, simulation frameworks, and workload characterization tools for AI training and inference.

The engineer will model workloads across compute, memory, and communication subsystems; identify bottlenecks, scalability limits, and architectural trade-offs; and evaluate hardware features, parallelization strategies, and system configurations for performance, power efficiency, and total cost of ownership. The role involves correlating model predictions with silicon measurements and software traces, and collaborating with architecture, microarchitecture, compiler, runtime, hardware, and systems teams to inform future platform decisions.

The position uses a hybrid work model, with three days per week in the office.

Requirements

• Master's degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or a related field and 3+ years of technical engineering experience; or bachelor's degree in one of these fields and 5+ years of technical engineering experience; or equivalent experience.
• Ability to meet Microsoft, customer, and/or government security screening requirements, including the Microsoft Cloud Background Check upon hire or transfer and every two years thereafter.
• Strong understanding of GPU and AI accelerator architectures, including compute pipelines, memory hierarchies, interconnects, and parallel execution models.
• Experience developing analytical performance models, roofline models, architectural simulators, or system-level performance estimation frameworks.
• Knowledge of AI training and inference workloads, including transformers, attention mechanisms, MoE architectures, and large-scale distributed execution.
• Experience modeling compute, memory bandwidth, communication collectives, and computation-communication overlap in accelerator-based systems.
• Familiarity with tensor, expert, pipeline, data, and context parallelization strategies and their performance implications.
• Strong programming skills in Python and C/C++, including experience developing modeling frameworks, simulators, and performance analysis tools.
• Experience with performance profiling, benchmarking, and correlating analytical models with hardware measurements and software traces.
• Experience evaluating architectural trade-offs using performance, power efficiency, scalability, and TCO metrics.
• Strong analytical, problem-solving, and communication skills, including the ability to translate performance insights into architectural recommendations.

Nice To Haves

• Doctorate in Electrical Engineering, Computer Engineering, Mechanical Engineering, or a related field and 3+ years of technical engineering experience; or master's degree in one of these fields and 6+ years of experience; or bachelor's degree in one of these fields and 8+ years of experience; or equivalent experience.
• Familiarity with AI agentic systems and flows for performance modeling and analysis.
• 2+ years of experience in Computer Architecture, AI Systems, or closely related technical domains.
• Familiarity with PyTorch, GPU programming, and modern AI inference or training frameworks such as vLLM and SGLang, as well as distributed runtime systems.

Responsibilities

• Develop and enhance analytical performance models and simulation frameworks for AI accelerators, GPU architectures, and large-scale AI systems.
• Model end-to-end AI training and inference workloads, including compute kernels, attention, MoE, memory access patterns, and communication operations.
• Evaluate architectural features, compute and memory hierarchies, interconnects, and collective communication mechanisms to identify performance and efficiency improvements.
• Analyze workload mapping and parallelization strategies across tensor, pipeline, expert, data, and context parallelism to optimize accelerator utilization and scalability.
• Develop system-level performance models to evaluate throughput, latency, perf/W, and TCO across accelerator, rack, and cluster configurations.
• Correlate analytical model predictions with silicon measurements, kernel benchmarks, and software traces to validate assumptions, improve model fidelity, and identify bottlenecks.
• Build and automate performance modeling, design-space exploration, sensitivity analysis, and visualization tools.
• Collaborate with hardware and software teams to translate modeling insights into architecture recommendations and hardware-software co-design opportunities.

Benefits

• Certain roles may be eligible for benefits and other compensation; additional benefits and pay information is available through the Microsoft careers pay information page.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior