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Overview
With more than 45,000 employees and partners worldwide, the Customer Experience and Success (CE\&S) organization is on a mission to empower customers to accelerate business value through differentiated customer experiences that leverage Microsoft’s products and services, ignited by our people and culture. We drive cross\-company alignment and execution, ensuring that we consistently exceed customers’ expectations in every interaction, whether in\-product, digital, or human\-centered. CE\&S is responsible for all up services across the company, including consulting, customer success, and support across Microsoft’s portfolio of solutions and products. Join CE\&S and help us accelerate AI transformation for our customers and the world.
The Experience Innovation Office (XIO) drives execution excellence across CE\&S, delivering AI\-powered solutions, accelerating product releases, and enabling teams to ship with quality and velocity. We partner with engineering, product, and leadership across multiple active programs, driving coordination, delivery, and operational readiness at scale.
We’re a team of Technical Program Managers who measure outcomes rigorously and continuously improve. We believe the highest\-impact program management happens close to the technical work, not in extra coordination layers or status meetings. We use AI, automation, and prototyping as force multipliers to expand what a TPM can deliver.
We're looking for a Senior Technical Program Manager, AI Execution Architecture who can own end\-to\-end delivery of complex technical programs from within the work, not from the sidelines. You'll operate deep in the technical details, drive technical discussions and decisions, and use prototyping and AI\-augmented execution to keep programs on track across engineering, product, and business partners.
This is a high\-impact, fast\-moving domain. Requirements evolve as technology advances, and the playbook is still being written. You'll need to be comfortable shaping the path as you walk it.
The primary work location for this position is Redmond, Washington, with a hybrid work requirement of three days per week in the office. However, this role is also open to candidates located anywhere in the United States and may be performed remotely.
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
In this role, you will:
- Own program delivery end\-to\-end — Serve as a TPM for cross\-functional programs, partnering closely with engineers, product managers, and leadership to drive day\-to\-day execution, ensuring release readiness through consistent application of milestones, dependency management, risk mitigation, and quality checkpoints as products and requirements evolve.
- Drive technical execution at scale — Operate across engineering and product teams to maintain integrated program plans, manage dependencies and resource tradeoffs, drive execution against milestones, and communicate progress, risks, and decisions to stakeholders and leadership. Establish and track success metrics for program health and efficiency, and ensure technical discussions result in clear actions, owners, and follow\-through.
- AI\-Augmenting — Apply AI to accelerate execution to use tools such as GitHub, GitHub Copilot, AI assistants, and low\-code platforms to prototype solutions, automate workflows, and improve delivery outcomes. Operate with technical depth to understand system architecture, APIs, data flows, and integration patterns well enough to drive informed decisions and shape technical direction without owning production engineering.
- Build the playbook — This is an evolving discipline. You'll define processes, create frameworks, and establish program management infrastructure for AI\-augmented delivery. Much of what you build will be first\-of\-its\-kind.
- Other — Embody our culture and values.
Qualifications
Required/Minimum Qualifications* Bachelor's Degree AND 4\+ years experience in engineering, product/technical program management, data analysis, or product development
+ OR equivalent experience.
- 2\+ years of experience managing cross\-functional and/or cross\-team projects.
Additional or Preferred Qualifications* Bachelor's Degree AND 8\+ years experience in engineering, product/technical program management, data analysis,
+ OR product development
+ OR equivalent experience.
- 6\+ years of experience managing cross\-functional and/or cross\-team projects.
- 1\+ year(s) of experience reading and/or writing code (e.g., sample documentation, product demos).
- Demonstrated ability to engage in technical discussions, understand system architectures and dependencies, and work effectively within technical environments to support delivery.
- Proven ability to translate ambiguity into clear, structured decisions by evaluating tradeoffs, questioning assumptions, and applying data‑driven, analytical reasoning.
- Well‑developed capability to lead end‑to‑end execution across engineering, product, and business partners, proactively managing dependencies, risks, and tradeoffs to deliver outcomes.
- Ability to translate complex technical discussions into clear decisions, well‑defined requirements, ownership models, and actionable execution plans that enable successful delivery.
Technical Program Management IC4 \- The typical base pay range for this role across the U.S. is USD $119,800\.00 \- $234,700\.00 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 $160,200\.00 \- $261,000\.00 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 $119K-$261K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 in Demand for This Role
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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($190K) sits 6% above the category median. Disclosed range: $119K to $261K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Microsoft AI Hiring
Microsoft has 17 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Product Manager, AI Software Engineer. Positions span Redmond, WA, US, Mountain View, CA, US, Dallas, TX, US. Compensation range: $219K - $304K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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