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About This Role
Overview
Platform shifts define eras in technology. In the era of agentic applications, the platform will be defined by innovations across models, tools, data, and runtime systems that reason, act, and continuously improve, as a single, integrated system. This shift is changing how enterprises evaluate cloud platforms, how developers build, and how IT secures and operates modern systems at scale.
We are seeking a Senior Director Product Marketing, Azure Cloud and AI Platform to drive this market transition and establish Azure as the platform of choice for strategic agentic deployments. This leader will define the technical model enterprises use to assess agentic platforms—capturing combined value across developer tools, AI, data, infrastructure, security, and operations—and translate it into durable leadership in the market, with proof points, competitive positioning, technical demos and go\-to\-market motions that scale across Microsoft.
This role operates at the executive layer across Engineering, Product, and Go\-to\-Market—partnering with leaders in Commercial Cloud \& AI, Security, Data \& AI, Industry, and the field—to align priorities and ensure Azure’s agentic platform value is consistently communicated to customers, partners, analysts, and Microsoft executives.
Reporting to the Azure Core and Platform General Manager, this role leads a team of Product Marketing leaders and a cross\-functional virtual team. Success will be measured by market leadership (category definition and analyst/customer perception), growth of Azure for strategic workloads, and the ability to drive cross\-platform adoption across the end\-to\-end stack. A core responsibility is to build and scale a demo platform and technical demo center of excellence aligned to the narrative and top customer use cases—used across the field, flagship events, keynotes, and digital surfaces.
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.
In alignment with our Microsoft values, we are committed to cultivating an inclusive work environment for all employees to positively impact our culture every day.
Responsibilities Market Shift, Category Strategy \& Executive Narrative
- You and your team will define and drive category creation for enterprise agent platforms, including the language, proof points, and point of view that shape how the market evaluates platform offerings that will support the next decade of applications.
- You will create durable executive\-ready programs, customer facing assets, leadership communications, analyst engagements, and major events keynotes and sessions—anchored in customer outcomes and measurable differentiation.
- You and your team will define and shape the strategic positioning that elevates Azure as the platform for agentic applications, spanning build, run, secure, govern, and optimize.
Enterprise Evaluation Model \& Technical Marketing Framework
- Define the technical model enterprises use to evaluate agentic platforms, including reference architectures, workload patterns, and decision criteria that bring together capabilities for AI, data, infrastructure, security, and operations.
- Translate that model into scalable marketing assets: messaging frameworks, competitive comparisons, executive briefs, field guidance, and partner narratives that create a consistent evaluation lens.
- Drive cross\-organization alignment to create combined value that lands as one story influencing launches, campaigns, events, customer conversations and solution plays.
Customer Zero Story \& Frontier Adoption Program
- Develop and scale a complete Customer Zero narrative that demonstrates how Microsoft adopts and benefits from its own platform and AI technologies from how we build new services, to how we operate at scale, to how we modernize DevOps and operational practices.
- Partner across Engineering, Operations, Security, and Product to capture measurable proof points (velocity, reliability, cost efficiency, and security posture) and translate them into customer\-ready stories, demos, and guidance.
- Build a customer and partner\-facing program that establishes Microsoft as a Frontier organization and provides a clear blueprint for how other businesses can achieve similar impact.
People Leadership \& Team Development
- Build and lead a high\-performing PMM team with a high bar for strategic thinking, technical fluency, and executive communication.
- Set vision, priorities, operating cadence, and mechanisms to deliver repeatable outcomes across narrative, launches, enablement, and demo execution.
- Develop talent and succession—coaching leaders to operate across ambiguity, drive influence at scale, and deliver market\-shaping impact.
Orchestration \& Influence
- Operate as an executive integrator, driving alignment across product groups and functions without direct authority and representing marketing in senior decision forums.
- Establish shared narratives, frameworks, and success metrics that scale across Azure and adjacent businesses while enabling local execution.
- Serve as a trusted advisor to senior leaders on market perception, competitive dynamics, and the implications of platform choices for strategic workloads.
Field, Partner \& Ecosystem Enablement
- Ensure platform value and leadership criteria, technical demos translate into actionable value propositions, plays, and guidance for sales, partners, and ISVs.
- Own the demo platform strategy aligned to narrative and top customer scenarios; drive readiness and reuse across field sellers, major events, keynotes, and digital surfaces.
- Build feedback loops with customers, partners, and the field to continuously refine messaging, demos, and GTM motions to increase cross\-platform adoption.
Platform\-Level Competitive Initiatives \& Differentiation
- Support and shape platform\-level competitive initiatives, ensuring Azure go\-to\-market functions have clear, compelling differentiation for why customers should choose Azure over an expanding set of cloud and AI\-first platform competitors.
- You and your team will deliver scalable competitive assets (battlecards, executive briefs, comparison frameworks, objection handling, and proof points) aligned to the enterprise evaluation model and top agentic workload scenarios.
- Set market direction and drive leadership for this emerging category by aligning product roadmap signals, customer evidence, and analyst narratives into a consistent point of view that the field can execute.
Market Insight \& Competitive Perspective
- Develop an integrated view of competition across cloud platforms, AI ecosystems, developer tools, and enterprise agent frameworks—anticipating how buyers will compare end\-to\-end stacks.
- Define the differentiation and proof points that support strategic workload growth on Azure, including partner signals, customer adoption patterns, and analyst/market feedback.
Other
- Embody our Culture and Values
Qualifications Required/minimum qualifications
- Master's Degree in Marketing, Computer Science, Business or related field AND 6\+ years experience in product marketing experience owning positioning, messaging/go\-to\-market strategy for technical products and platforms
- + OR Bachelor's Degree in Marketing, Computer Science, Business or related field AND 8\+ years experience in product marketing experience owning positioning, messaging/go\-to\-market strategy for technical products and platforms
+ OR equivalent experience.
- 6\+ years people management experience.
Additional or preferred qualifications
- Master's Degree in Marketing, Computer Science, Business or related field AND 12\+ years experience in product marketing experience owning positioning, messaging/go\-to\-market strategy for technical products and platforms
- + OR Bachelor's Degree in Marketing, Computer Science, Business or related field AND 15\+ years experience in product marketing experience owning positioning, messaging/go\-to\-market strategy for technical products and platforms
+ OR equivalent experience.
- 8\+ years people management experience.
- Experience influencing and driving outcomes across large, highly matrixed organizations at executive levels
- Experience creating executive narratives and technical frameworks that connect developer workflows to enterprise outcomes
- Experience marketing platforms, ecosystems, or horizontal technologies where value spans multiple product areas
- Experience with cloud platforms and modern application architectures; Experience across AI, data, infrastructure, security, and operations
- Experience with developer tools and technical storytelling for builders (e.g., workflows, reference architectures, hands\-on enablement)
- Experience building demo platforms or technical showcases used in keynotes, major events, and digital experiences
Product Marketing 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: $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. Director-level AI roles across all categories have a median of $243,000. This role's midpoint ($229K) sits 28% above the category median. Disclosed range: $155K to $303K.
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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