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About This Role
Overview
We are seeking a highly experienced leader to lead our US Federal Customer Success Unit (CSU) Cloud and AI Platforms business. This role will drive business and technical leadership across an organization of Cloud & AI Platform Cloud Solution Architects (CSAs) serving the US Federal Government.
Microsoft is on a mission to empower every person and every organization on the planet to achieve more. Our culture is centered on embracing a growth mindset, a theme of inspiring excellence, and encouraging teams and leaders to bring their best each day. Growth mindset encourages each of us to lean in and learn what matters most to our customers, to create the foundational knowledge that enables us to make customer-first decisions in everything we do. In doing so, we create life-changing innovations that impact billions of lives around the world. You can help us achieve our mission.
Responsibilities Business Leadership
- Provides strategic leadership on value-based delivery of well-architected (e.g., secure, resilient, artificial intelligence [AI]-enabled) solutions to grow Azure consumption across the Federal landscape by emphasizing the importance of adoption economics and delivering true customer value.
- Provides leadership to define the execution plan to connect sales planning and execution through delivery planning and execution, aligned with customer and business priorities to achieve consumption and usage targets, ensuring robust security oversight.
- Drives collaboration across the US Federal team in delivering Customer Success for our customers, with the help of the broader team (e.g., Services, partners, CSU team, Account Team Unit, Solution Team Unit, Engineering), engaging with customers to elicit feedback and agreement, and ensuring integration with the broader account plan process.
- Cultivates an active, global network of partners through orchestration with Global Partner Solutions (GPS) to drive consumption, usage, and deliver customer value.
- Shares a vision to guide orchestration across Solution Areas and stakeholders (e.g., Specialist Team Unit [STU], Customer Success Unit [CSU], Account Team Unit [ATU], Global Partner Solutions [GPS] within the US Federal org) to drive usage and consumption outcomes.
- Ensures business financial performance realization across the Federal business through an emphasis on operational excellence and rigor (e.g., pipeline management, execution, ownership of customer journey).
- Facilitates the model and leads comprehensive integration of Services resources to deliver on comprehensive consumption execution model.
Technical Leadership
- Advocates to drive customer technical requirements with corporate product and engineering teams. Creates an internal feedback loop to drive broad, systemic continuous improvement for customers across Microsoft (e.g., security, product support).
- Builds and maintains a network with senior technical decision makers (e.g., Chief Information Officer [CIO], Chief Information Security Officer [CISO], Chief Technology Officer, Chief Architect, technical fellows) to provide technical and operational guidance on technology trends, bridge understanding of identity, security, compliance, operational and risk requirements and influence decision making across relevant teams (e.g., CIO, CISO), and accelerate continuous cloud adoption at scale.
- Leverages deep technical knowledge, technical team members, and peer connections to establish and elevate trusted and influential relationships with customer decision makers (e.g., Chief Executive-level) for large and/or high-impact customers to drive customer adoption in mission critical areas.
- Leverages credibility and expertise to act as executive-level technical sponsor and leader for customers and partner ecosystem to continuously evolve and grow through the digital transformation journey (e.g., increase usage/consumption to drive customer expansion, upsell and renewal recapture rates).
- Supports Customer Success Unit (CSU) community-building internally and externally by providing community thought leadership and orchestrating with professional network to promote learning.
Business Execution
- Creates culture to drive operational and pipeline excellence, with particular focus on the key performance indicators (KPIs) covering operating model metrics (e.g., growth, velocity, efficiency, resource allocation) to ensure consistent execution against strategy.
- Escalates to ensure removal of blockers related to the customer business and/or internal processes to deliver technical and operational excellence in Customer Success teams and accelerate customer adoption.
- Leads area-level/global-level rhythm of business (ROB) and instills a sense of urgency for strategic agility through other leaders. Identifies widespread or global patterns in blockers and develops strategies to resolve and prevent them in the future.
- Assesses and prioritizes high-impact technical blockers, and coaches team to enable resolution.
- Drives consumption and usage rigor and discipline by developing and leading inclusive and regular rhythm of business (ROB) processes for the broader US Federal team.
- Drives customer engagement by ensuring implementation and growth of a predictable and scalable customer engagement model across all enterprise accounts (e.g., Strategic, Major) for the business, leveraging the Customer Success Unit (CSU) team, partners, and Microsoft Services.
- Leaders a large team of leaders to ensure customers achieve desired business outcomes and optimization of their investment with Microsoft. Ensures the organization expands learning on new adoption frameworks and new platform capabilities that cross both technical and business value conversations, including security and resiliency.
- Ensures the team identifies the right partners to delivery and execute on customer vision.
People Management and Culture Leadership
- Delivers people management success through modeling, coaching, and caring.
- Invests in the growth of others.
- Promotes intentional talent management strategy and champions a culture of customer-centricity, accountability, collaboration, and achieving big bold goals.
- Defines long-term strategies aimed at change and cultural transformation within the area of accountability as a coach, mentor, or program leader.
- Improves existing initiatives and capabilities needed to drive success within the scope of accountability and actively works to develop/mentor/coach talent internally or develop plans to attract needed talent to Microsoft.
- Provides vision and leadership in the strategic development and implementation of succession and development plans that enhance technical sales acumen and capabilities across the organization.
Qualifications
Required Qualifications
- Master's Degree in Business Management, Engineering, or related field AND 12+ years experience in technology leadership, consulting/ professional services, product strategy, business leadership, sales, or directly within Customer Success OR Bachelor's Degree in Business Management, Engineering, or related field AND 14+ years experience in technology leadership, consulting/professional services, product strategy, business leadership, sales, or directly within Customer Success + OR equivalent experience.
- 8+ years engineering and/or technology experience (e.g., technology field, Services, Consultant, Specialist Team Unit, Technical Sales, system integrators, developer, project manager, sales).
- 5+ years people management experience.
Other Requirements:Security Clearance Requirements: Candidates must be able 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: - The successful candidate must have an Active U.S. Government Top Secret Security Clearance. Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. Failure to maintain or obtain the appropriate clearance and/or customer screening requirements may result in employment action up to and including termination.Clearance Verification: This position requires successful verification of the stated security clearance to meet federal government customer requirements. You will be asked to provide clearance verification information prior to an offer of employment.Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.Citizenship & Citizenship Verification: This position requires verification of U.S citizenship due to citizenship-based legal restrictions. Specifically, this position supports United States federal, state, and/or local United States government agency customers and is subject to certain citizenship-based restrictions where required or permitted by applicable law. To meet this legal requirement, citizenship will be verified via a valid passport, or other approved documents, or verified US government clearance.
Preferred Qualifications
- Master's Degree in Business Management, Engineering, or related field AND 14+ years experience in technology leadership, consulting/ professional services, product strategy, business leadership, sales, or directly within Customer Success OR Bachelor's Degree in Business Management, Engineering, or related field AND 16+ years experience in technology leadership, consulting/ professional services, product strategy, business leadership, sales, or directly within Customer Success + OR equivalent experience.
- 7+ years people management experience.
- 4+ years experience in professional services, customer success, sales, and/or customer service.
- 4+ years experience in complex project management and/or customer account management (e.g., leading/orchestrating virtual teams).
- Federal cloud compete and positioning experience
Customer Success Unit Management M6 - 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. Mid-level AI roles across all categories have a median of $193,200. 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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