Interested in this AI/ML Engineer role at Microsoft?
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Overview
Are you inspired to empower organizations to transform the way they work by harnessing the full potential of artificial intelligence? We are seeking individuals to help guide customers through the evolving digital landscape, enabling them to unlock new opportunities, enhance productivity, and deliver exceptional employee and customer experiences. Join us in integrating advanced AI capabilities across devices, cloud platforms, and everyday business applications to help organizations realize seamless, innovative, and secure solutions that drive sustained growth and success in the AI era.
In the AI Workforce Specialists team, we are looking for passionate, experienced, and credible specialist sellers with a drive for developing and winning strategic opportunities that deliver end\-to\-end AI Workforce transformation at scale through high\-impact, value\-driven customer engagements—helping organizations achieve meaningful business outcomes and unlock the full potential of AI\-powered productivity. As a Senior Sales Specialist – AI Workforce, you will lead the charge in transforming how organizations adopt and scale AI\-powered productivity across AI Workforce solutions (M365 Copilot, Copilot Studio, Copilot Chat, Agents, Viva, ME3, ME5, ME7, Frontline Worker, Windows 365, AVD, Windows 365 Link). You will build strategies with customers, collaborating across different groups inside the Customer environment to successfully enable them to drive and adopt AI transformation. You will also lead consultative customer conversations and collaborate on the planning, orchestration and execution of end\-to\-end AI Workforce solutions opportunities with internal stakeholders and partners to cross\-sell and up\-sell. This role empowers you to shape customer intent, build pipeline, and deliver measurable impact while deepening your expertise in AI sales motions and contributing to a high\-performance, learning\-driven culture.
We are currently looking for Senior Sales Specialist – AI Workforce professionals to join our teams across various business groups, for varying customer sizes, in our enterprise, regulated, and partner services organizations. By applying to this role, you will be considered for multiple opportunities within Microsoft across the United States including locations beyond where the role is posted. This role is flexible in that you can work up to 50% from home. Travel percentages will vary according to role.
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.
Responsibilities
- Sales Execution: You will orchestrate a virtual team and assess customer needs to develop strategies that proactively build a stakeholder network to accelerate and close AI Workforce opportunities.
- Business Value \& Consultative Selling: You will foster and expand Microsoft’s relationship with Customer Business Decisions. Hunt new AI Workforce opportunities by identifying and engaging with key business contacts. Understand customers’ business and technology priorities, governance, decision and budget processes, and land the value proposition of AI Workforce solutions.
- Scaling and Collaboration: You will lead the planning, orchestration and execution of AI Workforce opportunities with internal stakeholders and partners to cross\-sell and up\-sell.
- Technical Expertise: You will lead AI Workforce BDM and ITDM conversations, share best practices, present solutions with a differentiated value proposition, use cases and key competitor knowledge across solution areas acting as a subject matter expert to inform decisions on pursuit or withdrawal.
- Sales Excellence: You will lead and plan for accounts across territories, do compete plans and business analysis to pursue high\-potential customers and manage AI Workforce solutions across the organization.
- Embody our culture and values
Qualifications Required Qualifications
- Bachelor's Degree in Computer Science, Information Technology, Business Administration, Information Security, or related field AND 4\+ years of experience in technology\-related sales or account management
+ OR equivalent experience.
Other Requirements
- Microsoft is unable to sponsor a work visa for this role due to the nature of the role’s job duties.
Preferred Qualifications
- Master's Degree in Business Administration (i.e., MBA), Information Technology, Information Security, or related field AND 8\+ years experience in technology\-related sales or account management
+ OR Bachelor's Degree in Computer Science, Information Technology, Business Administration, Information Security, or related field AND 12\+ years experience in technology\-related sales or account management
+ OR equivalent experience.
- 6\+ years solution or services sales experience.
- 6\+ years of experience forecasting, developing demand and building pipeline, and leading/orchestrating complex, matrixed teams.
- 2\+ years of experience with Generative and Agentic AI
- Experience with Microsoft Copilot and/or Generative AI Technologies
- Familiarity with AI/ML concepts and Copilot architecture
- Experience building AI Agents
- Strong communication and customer engagement skills
Solution Area Specialists IC4 \- The typical base pay range for this role across the U.S. is USD $107,600 \- $187,500 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 $145,600 \- $205,600 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 $107K-$205K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($156K) sits 28% below the category median. Disclosed range: $107K to $205K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Microsoft AI Hiring
Microsoft has 29 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Research Scientist, AI Product Manager. Positions span US, Redmond, WA, US, Dallas, TX, US. Compensation range: $143K - $304K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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