Microsoft

Principal AI Accelerator Tools Development Engineer

Microsoft • $142K — $274K *
Mountain View, CA 94040In-Person
Information Technology
5 - 7 years of experience
Today
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Job Overview by Ladders

Qualifications

  • Master's or Bachelor's in Electrical/Computer/Mechanical Engineering or related field with significant technical experience.
  • 7-8 years of experience in developing and optimizing AI workloads on GPUs or HPC platforms.
  • Proficiency in C++, PyTorch, and Triton for performance-focused software development.
  • Experience in analyzing performance and optimizing workloads for AI accelerators or distributed systems.
  • Knowledge of compiler technologies and automated validation frameworks.

Responsibilities

  • Design and develop frameworks for stress and performance testing of AI platforms.
  • Create workload generation infrastructure to stress various system resources.
  • Develop reusable tools using programming languages such as PyTorch and C++.
  • Create synthetic workloads that mimic real-world AI deployment scenarios.
  • Build automated systems for workload deployment and analysis.
  • Analyze performance across hardware-software stacks to eliminate bottlenecks.
  • Partner with compiler teams to enhance workload optimization strategies.

Benefits

  • Comprehensive health and wellness benefits.
  • Professional development opportunities and support.
  • Flexible work environment with remote work options.
  • Access to cutting-edge technologies and projects in AI.
  • Inclusion in a collaborative and innovative team culture.
Full Job Description
Responsibilities

AI Workload & Stress Tool Development

  • Design and develop scalable stress, performance, and validation frameworks for MAIA AI accelerator platforms.


  • Build workload generation infrastructure capable of exercising compute, memory, interconnect, networking, storage, and system-level resources.


  • Develop reusable stress tools using PyTorch, Triton, Python, C++, and custom MAIA SDKs.


  • Create synthetic and production-inspired workloads that model training and inference behaviors observed in large-scale AI deployments.


  • Build automated infrastructure for workload deployment, orchestration, telemetry collection, and result analysis.


Hardware-Aware Workload Optimization

  • Develop and optimize kernels targeting custom AI accelerators.


  • Create GEMM, attention, collective communication, and memory intensive stress workloads.


  • Analyze execution behavior across the hardware-software stack and identify bottlenecks impacting utilization and performance.


  • Collaborate with compiler and runtime teams to improve workload efficiency and hardware utilization.


Compiler & SDK Integration

  • Develop tooling that integrates with MAIA compiler pipelines, SDKs, runtime environments, and performance analysis tools.


  • Understand and debug compiler output, generated kernels, scheduling decisions, and execution behavior.


  • Build automation around model compilation, kernel validation, regression testing, and workload portability.


  • Partner with compiler teams to validate new compiler features and workload optimization strategies.


Platform Validation & Reliability

  • Design workload suites for platform bring-up, qualification, and reliability testing.


  • Build comprehensive regression infrastructure supporting silicon, firmware, system software, and platform releases.


  • Develop automated validation tools capable of identifying correctness, performance, thermal, power, and stability issues.


  • Enable platform readiness through scalable validation methodologies and continuous regression testing.


Performance Engineering

  • Characterize system performance across compute, networking, memory, and storage subsystems.


  • Develop benchmarking methodologies and performance dashboards.


  • Adapt and optimize industry-standard workloads including:


  • HPL/HPC benchmarks


  • LLM training workloads


  • Transformer-based inference workloads


  • Collective communication benchmarks


  • AI framework benchmark suites


  • Drive root-cause analysis and optimization initiatives across the stack.


Developer Productivity & Automation

  • Improve developer productivity through automation, CI/CD integration, diagnostics, and debugging infrastructure.


  • Build reusable tooling for workload generation, failure triage, telemetry analysis, and reporting.


  • Develop dashboards and automated workflows for large-scale validation environments.


  • Partner with engineering teams to convert recurring validation challenges into durable tooling solutions.


Qualifications

Required Qualifications:
  • Master's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 7+ years technical engineering experience
    • OR Bachelor's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 8+ years technical engineering experience
    • OR equivalent experience
  • 8+ years of experience developing and optimizing AI training and inference workloads for GPUs, AI accelerators, or HPC platforms, including distributed AI systems, compute-intensive kernel development, and performance-focused software development using frameworks such as C++, PyTorch and Triton.
  • 8+ years of experience analyzing and optimizing workloads on AI accelerator, GPU, or HPC platforms, including performance profiling, bottleneck analysis, and workload optimization, with knowledge of accelerator architectures, memory hierarchies, interconnects, runtime systems, and distributed AI infrastructure.
  • 8+ years of experience developing and optimizing GPU or AI accelerator kernels; building automated stress, validation, benchmarking, and reliability frameworks; and driving performance analysis and root-cause resolution across hardware, software, and distributed system environments.


Other Qualifications:

Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings:
  • Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter.


Preferred Qualifications:
  • Experience with AI compiler technologies and kernel generation frameworks, including LLVM, MLIR, Triton Compiler, or similar compiler toolchains.
  • Experience training, optimizing, or deploying large-scale AI models, including LLM training and inference workloads.
  • Experience with custom AI accelerator SDKs, collective communication libraries, and large-scale distributed computing environments.
  • Experience supporting silicon bring-up, platform qualification, post-silicon validation, or hardware/software integration activities and cloud-scale validation infrastructure


#azure #MAIA #AI/ML

Software Engineering IC5 - The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay

This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

About Microsoft

Microsoft is an American multinational corporation that develops, manufactures, licenses, supports, and sells a range of software products and services. Microsoft’s devices and consumer (D&C) licensing segment licenses the Windows operating system and related software, Microsoft Office for consumers, and the Windows Phone operating system. The company’s computing and gaming hardware segment provides Xbox gaming and entertainment consoles and accessories, second-party and third-party video games, and Xbox Live subscriptions; surface devices and accessories; and Microsoft PC accessories. Its phone hardware segment offers Lumia smartphones and other non-Lumia phones. Its D&C segment provides Windows Store, Xbox Live transactions, and Windows phone store; search advertising; display advertising; Office 365 Home and Office 365 Personal; first-party video games; and other consumer products and services as well as operating retail stores. Microsoft’s commercial licensing segments license server products, including Windows Server, Microsoft SQL Server, Visual Studio, System Center, and related Client Access Licenses (CALs); Windows Embedded; Windows operating system; Microsoft Office for business, including Office, Exchange, SharePoint, Lync, and related CALs; Microsoft Dynamics business solutions; and Skype. Its commercial segment offers enterprise services, including premier support services and Microsoft consulting services; commercial cloud comprising Office 365 Commercial, other Microsoft Office online offerings, Dynamics CRM Online, and Microsoft Azure; and other commercial products and online services. The company markets and distributes its products through original equipment manufacturers, distributors, and resellers, as well as online.

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Learn more about Microsoft
Size
181,000 employees
Market Cap
$1,762.4 billion
Industry
Enterprise Technology
Net Income
$51.3 billion
Founded
1975
5 Year Trend
+15.5%
Revenue
$153.2 billion
NASDAQ
MSFT
* Ladders Estimates

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