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About This Role
Overview
The Global Partner Go-To-Market (GTM), Programs, & Operations (GPO) organization within GCPS (Global Channel Partner Sales) brings together the go-to-market strategy, programs, and platforms to activate the Partner Channel to accelerate customers transformation with Secure AI and Agents.
The Global GCPS Cloud Solution Area (CSA) strategy team, within GPO, is a team of CSA aligned leaders focused on design and deliver CSA aligned partner execution strategy that accelerates CSP partners to deliver customer success while empowering the partner ecosystem. Within this team we are looking for a Cloud and AI Platform (CAIP) Channel Strategy Director to lead the GCPS Strategy and GTM Win Formula for CAIP GTM priorities (called Conversations in FY27). This role will focus on defining and mainstreaming our partner incentives, programs, sales strategy, readiness and resources to support the success of Azure and AI in the global market. The position will be responsible for working collaboratively across leaders and stakeholders across the company to translate the SME&C S(ME&C) CAIP strategy into Channel GTM plans. In addition, you will represent CAIP as a key stakeholder in MAICPP program planning (Designations, Specializations), partner capacity/training investments, incentive design and shaping the Connected AI narrative for partners.
As the CAIP GTM Strategy lead in GCPS, you will engage with Product Marketing Groups (PMG), Solution Area GTM teams (CSA), Global Sales and Operations (GS&O) to align priorities, represent Channel Partner insights and lead the development on a global Channel GTM Plan. Your role is to identify opportunities that strengthen partner outcomes, drive investment success, assess messaging, and develop engagement plan across the go-to-market journey to accelerate Azure Consumption Revenue (ACR) with a specific FY27 focus on the 4 strategic workloads: Fabric, Foundry, DBs and Github.
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
- Responsible for the end-to-end partner business planning and strategy for Microsoft CAIP GTM priorities assigned, translating Business Group (BG) plans and goals into partner execution and growth plans.
- Integrating high impact partner motions into our CSA team’s go-to-market motions, in collaboration with field leaders in Sales and Sales Execution and Operations.
- Set strategy for how we build, nurture, and engage partner ecosystem aligned to Solution Area. Perform partner planning, define scorecard metrics and key performance indicators (KPIs), and co-design the partner programs and investments, in partnership with Microsoft’s Product Marketing Group (PMG) and other stakeholders
- Proactively surface and resolve blockers to strategy execution and target achievement by leveraging partner and field feedback, diagnosing systemic gaps, and bringing prioritized asks to the Senior Leadership Team to secure the decisions, resources, or cross-functional alignment required to accelerate outcomes.
- Engage with global and local partners, PDM Managers (PDMM) and field leaders to gather feedback, share actionable insights and design/refine new strategies/programs to drive business growth through partners aligned to cloud strategy and programs
- Lead partner targeting/recruitment strategy based on partner technical capabilities and sales capacity requirements and partner practice development approach to drive the highest Partner return on investment (ROI) for Microsoft.
- Partner with finance and operations teams to track business progress and leverage monthly review cadence, Key Performance Indicator (KPI) reporting and ongoing business improvement
- Develop and land SME&C field guidance and execution strategy and practices across all partner sales and technical roles
- Perform scorecard and KPI analysis as data driven approach to draw trending, learning and insight. Engage with SME&C Worldwide and field personnel to proactively identify data driven business insights from monthly performance and effectiveness of the strategy and execution, as well as action plans to drive ongoing improvement across all partner GTM levers (recruit, build, GTM, sell-with, etc.)
- Put in place partner listening mechanisms to capture voice of partner. Capture feedback, synthesize and prioritize as an input to Industry engineering and business group plans.
- Embody our Culture and Value
Qualifications
Required/minimum qualifications
- Bachelor's Degree in Business Administration, Marketing, Finance, Engineering, or related field AND 5+ years experience in business consulting, sales, sales operations, information technology (IT), account management, business development, marketing, IT field sales, or a related field + OR equivalent experience.
- 6+ years experience in in Near Term Strategy (2 years out), Management Consulting, Sales, or Finance.
Additional or preferred qualifications
- 7+ years working directly with partner ecosystem, the ability to provide valuable business and technical guidance to them and to secure their commitment to use business solutions
- Demonstrated business acumen in public cloud and Microsoft Cloud & AI technologies, with deep knowledge of partner ecosystems and how partners develop, enhance, and scale cloud solutions.
- Ability to articulate both business and technical value of Microsoft cloud services to senior business and technical decision makers
- Proven organizational leadership with the ability to influence across teams, lead change initiatives, and effectively manage cross-functional virtual teams and partner relationships.
- Passionate, results-driven, and organized approach; with an attention to detail: A self-starter who can create and deliver scalable plans from concept to delivery, including the ability to prioritize and manage multiple projects, effectively driving to measurable results.
- Engagement expertise: Storytelling skills and passion for delighting partners, advocating on their behalf, creating great partner experiences, and understanding the linkage between these experiences and business success
- Executive Relations: The ability to interact confidently with leaders in a highly-matrixed organization to present/defend/clarify concerns or issues regarding an existing project, program, or solution. This includes the ability to confidently address difficult questions, handle pushback from a high-level audience, and maintain an executive demeanour while engaging in challenging or sometimes high-pressure situations
- Problem solving mentality leveraging internal and external resources, conflict resolution, and follow through with partners
Sales Strategy Enablement IC5 - The typical base pay range for this role across the U.S. is USD $130,900 - $251,900 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 - $272,300 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: $178K across 1623 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 9,079 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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($201K) sits 5% below the category median. Disclosed range: $130K to $272K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Microsoft AI Hiring
Microsoft has 62 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Software Engineer, Research Scientist. Positions span New York, NY, US, Kuala Lumpur, Federal Territory of Kuala Lumpur, Malaysia, Redmond, WA, US. Compensation range: $128K - $331K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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