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About This Role
Overview
Microsoft AI Experiences (AIX) is building the future of AI experiences across Copilot, Bing, Edge, and partner surfaces that reach hundreds of millions of people.
As AI products grow in reach and sophistication, access to compute and infrastructure is increasingly central to product strategy, customer experience, and business performance. We are looking for a strategic and analytical leader to shape how AIX plans, allocates, and manages the infrastructure that powers our AI experiences.
This role sits within the AIX Strategy organization and will lead cross-functional planning and decision-making for GPU capacity and related infrastructure investments. Working across Product, Engineering, Finance, and Infrastructure teams, you will develop forecasting models, establish resource-allocation frameworks, drive infrastructure governance, and ensure capacity investments are aligned to business priorities and product outcomes.
The ideal candidate combines strong strategic thinking, quantitative rigor, business judgment, and operational excellence. They are equally comfortable discussing infrastructure constraints with engineering teams, evaluating business tradeoffs with finance partners, and presenting recommendations to senior executives.
AI Experiences employees who live within a 50- mile commute of a designated Microsoft Hub in the U.S. are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.
Responsibilities* Lead forecasting and planning for GPU capacity and other AI infrastructure resources across AIX.
- Develop demand models that incorporate product roadmaps, user growth, model transitions, workload characteristics, and migration plans.
- Create and maintain frameworks for evaluating, prioritizing, and allocating constrained infrastructure resources across products and business priorities.
- Drive governance processes for infrastructure requests, allocation decisions, escalations, and executive reviews.
- Partner with Product, Engineering, Infrastructure, and Finance teams to connect infrastructure investments to business outcomes, including engagement, quality, revenue, efficiency, and customer experience.
- Build quantitative models, scenario analyses, and business cases to support investment and resource-allocation decisions.
- Define and improve the telemetry, reporting, and dashboards required to understand infrastructure utilization, capacity, throttling, costs, and business impact.
- Lead cross-organizational initiatives involving infrastructure migrations, workload transitions, capacity reallocations, and cost-optimization efforts.
- Identify capacity risks early and develop mitigation strategies that preserve business objectives while improving efficiency.
- Translate complex technical and operational information into clear recommendations and executive-ready communications. Qualifications Required Qualifications:
- Master's Degree in Business, Economics, Finance, Engineering, Computer Science, Data Science or related field AND 6+ years experience in strategy, business planning, finance, product management, operations, or program management,
+ OR Bachelor's Degree in Business, Economics, Finance, Engineering, Computer Science, Data Science or related field AND 8+ years of experience in strategy, business planning, finance, product management, operations, program management + OR equivalent experience.
- 6+ years of experience with AI infrastructure, cloud capacity planning, model serving, inference, training, or distributed systems.
- 6+ years of experience building business cases and ROI models for technology investments.
- 6+ years of experience partnering closely with Product, Engineering, and Finance organizations.
- 6+ years of experience with SQL, Python, Power BI, Kusto, Excel, or other analytical tools.
- Experience operating in fast-paced, highly ambiguous environments and creating structure where processes do not yet exist.
Preferred Qualifications:
- Master's Degree in Business, Economics, Finance, Engineering, Computer Science, Data Science or related field AND 12+ years of experience in strategy, business planning, finance, product management, operations, or program management
- 6+ years experience in Strategy, Management Consulting, and/or Finance.
- 6+ years of experience with AI infrastructure, cloud capacity planning, model serving, inference, training, or distributed systems.
- Experience building business cases and ROI models for technology investments.
- Experience partnering closely with Product, Engineering, and Finance organizations.
- Experience with SQL, Python, Power BI, Kusto, Excel, or other analytical tools.
- Experience operating in fast-paced, highly ambiguous environments and creating structure where processes do not yet exist.
Business Strategy IC6 - The typical base pay range for this role across the U.S. is USD $155,800 - $277,200 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 $202,400 - $303,600 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 $155K-$303K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($229K) sits 8% above the category median. Disclosed range: $155K to $303K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Microsoft AI Hiring
Microsoft has 69 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer, AI Software Engineer, AI Product Manager. Positions span Washington, DC, US, Mountain View, CA, US, TX, US. Compensation range: $143K - $331K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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