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Overview
At Microsoft AI, we are building the future of human\-AI interaction through Copilot—an AI companion designed to empower people and organizations to achieve more. Copilot is rapidly becoming a defining platform for how individuals create, work, and innovate, and Microsoft is uniquely positioned at the center of this transformation.
We are hiring a Senior Director, Executive Communications to support a senior executive (EVP) within the Copilot organization. This role sits at the intersection of executive storytelling, product positioning, and market influence—helping shape how both the leader and Copilot are perceived across key audiences. In this role you will expand the executive and product voice to more directly engage the innovator, developer, and startup ecosystem
This is a unique opportunity for someone who thrives in fast\-moving environments and understands how to build visibility, credibility, and narrative from the ground up. You will operate with high ownership and proximity to leadership, helping elevate an executive voice externally while positioning Copilot at the forefront of conversations with builders, creators, and innovators. From shaping executive presence on social platforms to influencing how Copilot shows up in the broader AI ecosystem, your work will directly impact talent attraction, partnerships, and long\-term market positioning.
The ideal candidate is a strategic and highly adaptable communications leader who blends executive counsel with hands\-on execution. You bring strong instincts for narrative development, understand how to engage modern technical audiences, and know how to build relevance in fast\-evolving markets. You are comfortable operating with ambiguity and understand how to position both a product and a leader in moments that matter. You understand the AI landscape and its constant shifts, and you can speak credibly about what is happening at the frontier of both consumer and enterprise AI products.
Responsibilities* Partner directly with a senior Copilot executive to build and evolve their voice, presence, and effectiveness as a communicator internally and externally.
- Develop and execute an executive communications strategy that positions the leader as a credible and visible voice within AI, startup, and creator ecosystems.
- Shape and amplify the Copilot narrative in the market, ensuring consistent and compelling positioning across channels and moments.
- Draft high\-impact content including social posts, keynote narratives, thought leadership, and external communications, with an emphasis on relevance in real\-time conversations (e.g., social media, industry forums). Operate with a rapid storytelling cadence tied to feature and product velocity, turning shipping moments into timely, differentiated narratives.
- Ensure the executive is actively engaged in the right cultural and industry conversations, particularly among founders, builders, and innovators.
- Drive meaningful connections within AI startup and developer communities, positioning Copilot as an active participant in innovation\-driven networks.
- Support strategic opportunities such as executive engagements, curated events (e.g., founder dinners), and ecosystem interactions to strengthen market presence and influence.
- Translate product priorities into clear, differentiated messaging that reinforces Copilot's role in shaping the future of AI across both consumer and enterprise contexts.
- Drive alignment across internal stakeholders to ensure consistency between executive messaging, product storytelling, and broader Copilot communications strategies.
- Monitor external sentiment and conversations to inform proactive narrative development and rapid response strategies.
- Provide ongoing counsel to leadership on communications strategy, positioning, and opportunities to strengthen visibility and impact.
Qualifications
Required Qualifications
- Bachelor's Degree in Business, Marketing, Communications, Finance, or related field AND 6\+ years communications, marketing operations, field operations, program management, project management, or related experience
- + OR equivalent experience
Preferred Qualifications
- Master's Degree in Business, Marketing, Communications, Finance or related field AND 8\+ years Communications, Marketing Operations, Field Operations, Program Management, Project Management or related experience
- + OR Bachelor's Degree in Business, Marketing, Communications, Finance or related field AND 10\+ years Communications, Marketing Operations, Field Operations, Program Management, Project Management
- 5\+ years people management experience
- Actively integrated into the San Francisco Bay Area tech and startup ecosystem
- Demonstrated fluency in the AI landscape, including the ability to speak credibly with founders and developers about both consumer and enterprise AI products
- A track record of building developer or startup community relationships and translating product momentum into ecosystem\-facing storytelling
- Proven experience working in a startup or working closely with the startup and product development community in a fast\-paced, high\-velocity environment.
Communications 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
Communications IC6 \- 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
Communications 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 el.igible 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: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 in Demand for This Role
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 $185,000 based on 13,200 positions with disclosed compensation. Director-level AI roles across all categories have a median of $250,000. This role's midpoint ($201K) sits 9% above the category median. Disclosed range: $130K to $272K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Microsoft AI Hiring
Microsoft has 20 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Research Scientist, Data Scientist. Positions span New York, NY, US, Mountain View, CA, US, San Francisco, CA, US. Compensation range: $151K - $331K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,258 tracked positions. That's 26% above the national 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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