Interested in this AI/ML Engineer role at Microsoft?
Apply Now →Skills & Technologies
About This Role
Overview
Microsoft Foundry helps organizations build, ground, evaluate, govern, and operate production AI agents and applications. The product portfolio spans developer tools, models, agent capabilities, context and knowledge, observability, governance, security, and the experiences that connect them.
As Senior Director, Azure AI Product Marketing, AI Narrative \& Growth, you will own the all\-up platform narrative, messaging and positioning framework (MPF), and core bill of materials (BOM) for Microsoft Foundry and the broader Microsoft agent platform story. This is a high\-impact, executive\-facing role for a leader who can simplify a complex portfolio into a durable story and turn that story into launches, field and partner assets, events, demos, executive communications, and customer conversations.
You will lead a high\-performing product marketing team and help manage a portfolio with product engineering and partner teams in GitHub, Microsoft 365, Microsoft IQ, Fabric, Copilot Studio, Agent 365, Security, sales, brand, communications, analyst relations, developer relations, and integrated marketing. The remit is intentionally weighted toward narrative and storytelling, with a product growth responsibility: using customer, market, competitive, field, and usage signals to sharpen priorities and determine whether the story is landing.
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
- Own the all\-up Microsoft Foundry and agent platform narrative, including the primary MPF, positioning hierarchy, messaging pillars, proof points, naming logic, and competitive differentiation.
- Lead the core platform BOM and story system across materials, solution plays, executive briefing content, field and partner readiness, web experiences, thought leadership, technical content, and demos.
- Connect GitHub, Microsoft Foundry, Copilot Studio, Microsoft IQ, Agent 365, Security, and adjacent data and AI products into a coherent end\-to\-end customer story while preventing duplicative or conflicting narratives.
- Translate the product roadmap into a small set of strategy\-relevant big rocks, and identify important market or customer themes that are not yet explicit on the roadmap. Use those insights to influence product and portfolio decisions.
- Set the editorial and storytelling quality bar across executive briefs, blogs, thought leadership, launch assets, social content, event story arcs, demos, customer evidence, and field materials.
- Lead narrative readiness for Tier 1 launches and major marketing moments, including Build, Ignite, AI Tour, executive briefing programs, and priority third\-party events. Align announcements, demos, creative, sessions, and field readiness around one story.
- Serve as a thought partner to executives and product and engineering leaders. Represent the platform with customers, analysts, partners, and the field, and shape analyst relations, public relations, compete, and executive communications.
- Establish a clear operating model across product marketing, engineering, brand, communications, events, developer relations, sales, and partner teams, including decision rights, review cadences, ownership, and escalation paths.
- Use a light\-touch growth lens to evaluate narrative performance, adoption, customer comprehension, competitive position, and field feedback. Focus on the insights and tradeoffs that should change strategy rather than owning campaign execution end to end.
- Lead, coach, and develop senior product marketers. Set clear priorities, create accountability and build an inclusive culture that combines high standards with sustainable execution.
- Manage portfolio priorities, marketing investments, and vendor capacity. Build disciplined editorial calendars, content governance, asset lifecycle management, launch readiness, and post\-launch learning loops.
- Identify the essential story, remove noise, and make complex technical and organizational choices understandable to executives, developers, sellers, partners, and customers.
- Build sufficient technical depth across agent platforms, models, context and knowledge, evaluation, observability, governance, security, and the developer stack to challenge assumptions and earn credibility with engineering.
- Lead across organizational boundaries where ownership is shared, incentives differ, and progress depends on influence rather than direct authority.
- Make clear priority and quality decisions during rapid product change, incomplete information, and high\-stakes launch windows.
- Create clarity and stability for the team through change while maintaining a high bar for craft, accountability, collaboration, and inclusion.
- 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, product strategy, or go\-to\-market leadership experience in enterprise cloud, AI, developer platforms, or related technology categories
- + OR Bachelor's Degree in Marketing, Computer Science, Business or related field AND 8\+ years experience in product marketing, product strategy, or go\-to\-market leadership experience in enterprise cloud, AI, developer platforms, or related technology categories
+ 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, product strategy, or go\-to\-market leadership experience in enterprise cloud, AI, developer platforms, or related technology categories
- + OR Bachelor's Degree in Marketing, Computer Science, Business or related field AND 15\+ years experience in product marketing, product strategy, or go\-to\-market leadership experience in enterprise cloud, AI, developer platforms, or related technology categories
+ OR equivalent experience.
- 8\+ years people management experience.
- Experience owning platform\-level narrative, messaging, positioning, and go\-to\-market strategy for complex, global products
- Experience leading high\-stakes launches and cross\-functional programs with product, engineering, sales, brand, communications, field, and partner teams.
- Experience turning signals into strategic recommendations and roadmap influence.
- Experience with AI agent platforms, foundation models, developer tools, (APIs), evaluation, observability, governance, security, and enterprise context or knowledge systems.
- Experience connecting multiple products and business groups into a single platform narrative and shared go\-to\-market system.
- Experience with analyst relations, executive briefings, public speaking, developer audiences, and C\-level customer conversations.
- Experience managing marketing investments, agencies, and vendors across global programs.
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: $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.