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About This Role
Overview
Microsoft's Corporate, External \& Legal Affairs (CELA) organization supports one of the most complex commercial operations in the world. The Customer \& Partner Solutions (CPS) group is where legal meets the commercial engine, supporting the Microsoft Commercial Business, Microsoft Elevate, the Deal Desk, and thousands of customer and partner interactions every year.
The Director, AI Operations sits at the center of that intersection. You will own the operational and technology strategy that enables CELA CPS to function as a modern, AI\-powered legal organization, developing the platforms our teams depend on daily and driving AI adoption at scale. You will also lead the small technology team that delivers this work, and will lead the virtual teams that carry connected\-service development across CPS.
This is not a policy role or a staff function. This is a delivery role in which you will be expected to move programs from concept to production, build trusted relationships across legal and engineering, and operate effectively across organizational layers.
Microsoft's mission is to empower every person and every organization on the planet to achieve more, and we're dedicated to this mission across every aspect of our company. Our culture is centered on embracing a growth mindset and encouraging teams and leaders to bring their best each day. Join us and help shape the future of the world.
ResponsibilitiesTeam Leadership
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You are the manager of the CPS technology team and the owner of its operating model. That means setting the direction, holding the leadership team to a shared definition of success, and making sure the work is treated as a team sport rather than a set of individual projects.
- Set the vision, definition of success, metrics, and benchmarking for CPS technology work, and establish leadership team alignment behind that model.
- Manage, coach, and set priorities for the CPS Legal Engineer and the CPS Content Lead, including talent assessment and role remapping as the model matures.
- Lead the virtual teams that carry connected\-service delivery, drawing resources from across CPS and the wider CELA organization.
- Own intake and tradeoff decisions across competing technology requests, including the weekly prioritization rhythm with the leadership team.
CPS Connected Systems
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Own and drive the FY27\+ platform strategy that expands upon our existing Microsoft Dynamics CRM and matter management platform. This includes partnering with engineering counterparts to translate legal\-operations requirements into delivered product, and then rolling it out to hundreds of legal professionals worldwide.
CELA Frontline Operations
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Oversee and expand the technology infrastructure that supports CELA's first\-response legal support function serving over 100,000 Microsoft Commercial Business and Microsoft Elevate clients. This includes further innovating the platform and implementing process improvements based on data and user experience.
AI Adoption and Third\-Party Solutions
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Lead the rollout and ongoing expansion of existing first\- and third\-party AI tools and solutions across CPS. This includes working closely with internal engineering teams and vendors, our legal professionals and our internal clients to drive deep technical integration and cultural adoption of these key solutions.
Executive Engagement and Transparency
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You will often function as the face of CPS technology work with internal and external executives. This includes leading regular internal business reviews, meeting with technology partners on build/buy assessments, and explaining our work with other legal technology teams worldwide.
Culture
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- Embody our culture and values.
- Foster inclusive practices and ensure diverse perspectives are heard.
- Support colleagues and clients with empathy and respect.
- Embrace a global mindset, respecting similarities and differences in others.
Qualifications
Required Qualifications
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- Bachelor's Degree in relevant field (e.g., Liberal Arts, Business Administration, Management, Computer Science) AND 8\+ years experience in financial management, business planning, operations management, strategy, project management, human resources or business\-related roles OR equivalent experience.
Preferred Qualifications
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- Master's Degree in relevant field (e.g., Liberal Arts, Business Administration, Management, Computer Science) AND 12\+ years experience in financial management, business planning, operations management, strategy, project management, human resources, or business\-related roles OR Bachelor's Degree in relevant field (e.g., Liberal Arts, Business Administration, Management, Computer Science) AND 15\+ years experience in financial management, business planning, operations management, strategy, project management, human resources, or business\-related roles OR equivalent experience.
- 5\+ years people management experience.
- 10\+ years of progressive experience in legal operations, legal technology, or a closely adjacent field, such as enterprise program delivery in a regulated industry.
- Demonstrated ability to own and ship technology programs end to end.
- Experience managing or leading a small team, including setting priorities, coaching, and assessing talent against a changing operating model.
- Experience operating in a large, matrixed global organization with multiple executive stakeholders.
- Ability to translate between legal, engineering, and commercial audiences without losing meaning in either direction.
- Comfort with ambiguity, as this role spans strategy and execution and you need to be effective at both.
- Familiarity with Microsoft's commercial and legal infrastructure (MCAPS, CPS, CELA).
- Hands\-on experience with AI legal tools such as Harvey or Copilot, and a genuine interest in driving AI adoption at scale.
- Experience managing vendor partnerships, including SSPA and security compliance reviews and license governance.
- Fluency in legal operations concepts: matter management, outside\-counsel governance, paralegal\-support models, and client intake.
- Prior work in content governance, communications strategy, or knowledge management.
Business Management M6 \- The typical base pay range for this role across the U.S. is USD $130,900 \- $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 $165,600 \- $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 $130K-$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. Disclosed range: $130K 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
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