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About This Role
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 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.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process.
Salary Context
This $142K-$304K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Microsoft, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $142K to $304K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Microsoft AI Hiring
Microsoft has 29 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Research Scientist, AI Product Manager. Positions span US, Redmond, WA, US, Dallas, TX, US. Compensation range: $143K - $304K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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