Interested in this AI/ML Engineer role at Microsoft?
Apply Now →Skills & Technologies
About This Role
Overview
Are you passionate about helping customers transform their businesses through AI, cloud modernization, application innovation, and data\-driven transformation?
The Cloud \& AI Platforms Solution Engineering organization partners with enterprise customers to accelerate AI adoption, modernize applications and infrastructure, unlock data value, and drive business transformation on Microsoft Cloud. As the Director of Solution Engineering for the Cloud \& AI Platforms team, you will lead a high\-performing organization of Solution Engineering Managers and Solution Engineers responsible for winning technical decisions, driving customer business outcomes, accelerating Azure growth, and enabling customers to become Frontier Firms through AI\-powered innovation.
This role combines people leadership, technical leadership, strategic business ownership, and customer engagement. You will work closely with Specialist Sales, Customer Success, Industry Solutions, Partners, Product Engineering, and Business Leaders to develop and execute growth strategies across some of Microsoft's largest enterprise customers.
As a manager of managers, you will be responsible for organizational performance, talent strategy, operational excellence, and execution against business objectives while cultivating a culture of accountability, inclusion, learning, and customer obsession.
Responsibilities
### Organizational Leadership \& Talent Development
- Lead, develop, and inspire a geographically distributed organization of Solution Engineering Managers and Solution Engineers.
- Establish a high\-performance culture focused on accountability, technical excellence, collaboration, inclusion, and continuous learning.
- Develop leadership bench strength through coaching, succession planning, manager development, and talent investment.
- Drive employee engagement, organizational health, diversity and inclusion initiatives, and retention strategies.
- Create career growth opportunities that strengthen both technical expertise and leadership capabilities across the organization.
### Business Leadership \& Strategy Execution
- Own technical strategy and execution across Cloud \& AI Platform solution areas, including AI, Data, Infrastructure, Applications, Development Platforms, and Modernization.
- Partner with business leaders to define territory strategies, organizational priorities, resource allocation plans, and growth objectives.
- Support achievement of strategic business goals through technical engagement, customer influence, and solution adoption.
- Leverage business insights, customer trends, and market opportunities to shape organizational priorities and execution plans.
- Drive disciplined execution through clear accountability, performance management, and operational rigor.
### Customer Impact \& Executive Engagement
- Build trusted advisor relationships with CIOs, CTOs, Chief Data Officers, Chief AI Officers, and other senior customer decision makers.
- Help enterprise customers envision and execute cloud, data, and AI transformation strategies that deliver measurable business value.
- Engage directly in complex strategic opportunities, executive briefings, architecture reviews, and customer transformation programs.
- Guide teams in resolving technical blockers and accelerating customer decision making.
- Represent Microsoft as a senior technology leader with strategic customers, partners, and industry stakeholders.
### Technical Leadership \& Innovation
- Lead technical strategy supporting Azure, Microsoft Fabric, Azure AI, GitHub, Foundry, Intelligent Applications, Modern Infrastructure, Security, and Application Modernization solutions.
- Drive technical excellence across architecture design, solution envisioning, proofs of concept, demonstrations, and deployment planning.
- Ensure customer solutions are secure, scalable, resilient, and AI\-ready.
- Foster innovation and adoption of emerging Microsoft technologies and AI\-powered experiences.
- Deliver field insights that influence product direction, engineering priorities, and go\-to\-market strategy.
### AI Transformation Leadership
- Champion Microsoft's vision for AI transformation and Frontier Firm enablement.
- Help customers accelerate adoption of generative AI, intelligent applications, copilots, agents, and data\-driven business processes.
- Guide teams in helping customers build AI roadmaps aligned to business outcomes.
- Position Microsoft as the trusted platform for enterprise AI innovation and responsible AI implementation.
- Create organizational capability in rapidly evolving AI technologies and customer scenarios.
### Partner \& Cross\-Functional Leadership
- Collaborate with Specialist Sellers, Customer Success Account Managers, Industry teams, Partner organizations, and Product Engineering.
- Ensure effective pod\-based selling motions and seamless technical orchestration across solution areas.
- Drive strategic partner alignment that expands customer value and accelerates transformation outcomes.
- Help shape regional and national business strategies through thought leadership and field feedback.
### Operational Excellence
- Manage organizational planning, headcount strategy, budget stewardship, workforce planning, and talent investments.
- Drive excellence in forecasting support, technical engagement quality, customer satisfaction, and business execution.
- Utilize data\-driven insights and performance metrics to improve organizational effectiveness.
- Implement scalable processes and operational rhythms that increase consistency and impact across teams.
- Ensure effective governance, compliance, and execution against Microsoft business priorities.
Qualifications Required/minimum qualifications
- Master's Degree in Computer Science, Information Technology, Business or related field AND 6\+ years technical pre\-sales or technical consulting experience OR Bachelor's Degree in Computer Science, Information Technology, or related field AND 8\+ years technical pre\-sales or technical consulting experience OR 10\+ years technical pre\-sales or technical consulting experience OR equivalent experience.
Other Requirements
- This position is not eligible for visa sponsorship. Candidates must have authorization to work in the United States that does not now or in the future require employer sponsorship.
Additional or preferred qualifications
- 10\+ years technical pre\-sales, technical consulting, or technology delivery, or related experience OR equivalent experience.
- 5\+ years people management experience (including leading virtual teams).
- Experience driving business growth through cloud platform, AI, data, application modernization, infrastructure modernization, security, developer technologies, or digital transformation initiatives.
- Deep executive communication, presentation, and customer engagement skills.
- Proven track record partnering across sales, customer success, engineering, and partner ecosysems to influence technical and business decision makers across complex enterprise environments.
- Experience supporting large strategic enterprise customers and executive\-level transformation initiatives.
- Passion for coaching, developing talent, and fostering a high\-performance team.
Solution Engineering 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: $175K across 2162 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($229K) sits 7% above the category median. Disclosed range: $155K to $303K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Microsoft AI Hiring
Microsoft has 42 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager, Data Scientist. Positions span US, CA, US, Redmond, WA, US. Compensation range: $147K - $331K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.