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About This Role
Overview
Microsoft Azure is a rapidly growing global cloud platform where trust, security, and innovation are foundational. As customers across government and regulated industries accelerate AI adoption, they increasingly require solutions that deliver advanced AI capabilities while supporting control over data, infrastructure, operations, and compliance. Azure Product Marketing brings these innovations to market through differentiated positioning, clear customer value, and go\-to\-market execution across engineering, sales, and partners.
The *Product Marketing Manager, Sovereign Cloud \& AI* partnerships will own product marketing for Azure capabilities and solutions that enable customers to deploy advanced AI across sovereign and regulated environments spanning public cloud, private cloud, and hybrid architectures. This role will lead positioning, messaging, launches, campaigns, and field enablement for a strategic portfolio focused on helping customers adopt AI with confidence, control, and resilience. The role will also help shape product direction through customer insights, competitive context, and market understanding.
You will play an important role in defining how Microsoft brings Sovereign AI solutions to market by combining Azure platform capabilities with private cloud options and partnerships across AI labs, model providers, and neocloud partners. You will work closely with cross\-functional stakeholders across engineering, product, marketing, sales, business development, and partner teams to grow your portfolio and accelerate customer adoption.
This role provides the opportunity to shape strategy and storytelling for a high\-growth category at the intersection of AI, cloud, and sovereignty. You will help customers modernize with advanced AI while meeting sovereignty, security, and regulatory requirements, and you will help define how Azure earns trust in some of the world’s most demanding environments.
This is a strategic opportunity to influence business results, contribute to a critical area of Microsoft’s AI and cloud strategy, and build deep expertise in one of the fastest\-growing areas of the market.
Responsibilities
- Partner closely with engineering, business development, and partner teams to shape roadmap priorities and go\-to\-market strategy for Sovereign AI solutions across public cloud, private cloud, and hybrid environments, grounded in customer, regulatory, and national requirements.
- Define differentiated positioning and messaging for Sovereign AI solutions that help customers adopt advanced AI while maintaining control over data, infrastructure, operations, models, and compliance.
- Develop customer evidence and technical proof points, including solution narratives, reference architectures, competitive insights, partner value propositions, and regulatory\-aligned messaging that can scale globally while remaining locally relevant.
- Work across Azure, partner, and ecosystem stakeholders to bring Sovereign AI solutions to market in collaboration with AI labs, model providers, neoclouds, and strategic infrastructure partners.
- Enable field and partner teams with launch materials, training, sales plays, and repeatable go\-to\-market assets that support customer conversations across sovereign cloud, AI innovation, and regulated industry requirements.
- Translate customer and market insight into product and business recommendations, helping influence solution strategy, partner priorities, and long\-term category development for Sovereign AI.
Qualifications Required/minimum qualifications
- Master's Degree in Marketing, Computer Science, Business or related field AND 4\+ years experience in product marketing, technical marketing, partner marketing, or technical sales for cloud, AI, security, or infrastructure products, including end\-to\-end launches and go\-to\-market execution
+ OR Bachelor's Degree in Marketing, Computer Science, Business or related field AND 6\+ years experience in product marketing, technical marketing, partner marketing, or technical sales for cloud, AI, security, or infrastructure products, including end\-to\-end launches and go\-to\-market execution
+ OR equivalent experience.
Additional or preferred qualifications
- Experience marketing cloud and AI solutions for governments, regulated industries, or other customers with heightened requirements around sovereignty, security, compliance, and operational control.
- Familiarity with AI platform and ecosystem concepts such as foundation models, inferencing, training environments, private AI infrastructure, hybrid architectures, and partner\-delivered AI solutions.
- Proven ability to collaborate with and influence stakeholders across engineering, product, sales, business development, legal, compliance, and partners to drive business impact.
- Experience creating differentiated messaging and technical content for decision makers and technical audiences, including business leaders, IT leaders, architects, data and AI teams, and partner sellers.
- Strategic thinking and prioritization skills, with the ability to lead complex, cross\-functional go\-to\-market workstreams from strategy through execution.
- Ability to manage multiple time\-sensitive projects in a fast\-moving market.
Product Marketing IC5 \- The typical base pay range for this role across the U.S. is USD $130,900\.00 \- $251,900\.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 $165,600\.00 \- $272,300\.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 $130K-$272K 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 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($201K) sits 13% above the category median. Disclosed range: $130K to $272K.
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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