Principal AI Accelerator Tools Development Engineer

Job description

Overview Microsoft Silicon, Cloud Hardware, and Infrastructure Engineering (SCHIE) is the team behind Microsoft’s expanding Cloud Infrastructure and responsible for powering Microsoft’s “Intelligent Cloud” mission. SCHIE delivers the core infrastructure and foundational technologies for Microsoft's over 200 online businesses including Bing, MSN, Office 365, Xbox Live, Teams, OneDrive, and the Microsoft Azure platform globally with our server and data center infrastructure, security and compliance, operations, globalization, and manageability solutions. Our focus is on smart growth, high efficiency, and delivering a trusted experience to customers and partners worldwide and we are looking for passionate engineers to help achieve that mission. Microsoft's Hardware Systems organization is developing AI-native silicon and hyperscale systems designed to power the next generation of frontier AI models. The MAIA platform combines custom silicon, high-performance networking, advanced compiler technologies, and large-scale system infrastructure to enable industry-leading AI training and inference. The Platform Systems Engineering (PSE) team is seeking a Principal AI Accelerator Tools Development Engineer to lead the development of next-generation stress, validation, and performance tooling for MAIA AI accelerator platforms. In this role, you will build software frameworks, stress workloads, and validation tools that exercise every layer of the AI stack, from hardware execution engines and memory subsystems to compiler-generated kernels, distributed communication fabrics, and large-scale AI workloads. Your work will play a critical role in platform bring-up, qualification, performance characterization, reliability validation, and fleet readiness for both current and future generations of MAIA systems. You will work closely with silicon architects, compiler teams, runtime developers, performance engineers, validation teams, and AI framework developers to translate platform requirements into scalable tooling and workload solutions. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. 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