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About This Role
Overview
Are you passionate about AI and eager to transform the IT landscape? Join our dynamic team as a Sales Specialist\-Cloud \& AI Platforms to lead AI transformation and help customers modernize infrastructure, optimize operations, and drive innovation with Azure’s advanced AI capabilities. You’ll be part of an inclusive, high\-performing, and customer\-obsessed team where collaboration, connection, and continuous learning are at the heart of everything we do.
As a Sales Specialist\-Cloud \& AI Platforms, you'll be at the forefront of AI growth and disruption, guiding customers through their AI journey and helping them achieve their strategic goals. You'll ensure customers can fully harness the transformative potential of AI to stay ahead of the competition. If you're ready to make a significant impact and drive AI transformation, we invite you to join us and be part of this exciting journey!
This opportunity enables you to:
- Accelerate your career by leading high\-impact Cloud \& AI solutions.
- Develop technical and consultative selling skills.
- Build deep business acumen and future\-ready capabilities.
- Strengthen leadership through collaboration and best practice sharing.
We are currently looking for Sales Specialist\-Cloud \& AI Platforms, 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 very 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
Customer Engagement:* Ensures each customer understands Microsoft's commitment to secure, AI\-powered transformation and aligns the customer’s related priorities to the customer success plan, fostering long\-term partnerships through AI\-driven insights. Ensures partner teams, resources and relationships are activated, driving co\-selling strategies and partner attach to each opportunity early in the sales lifecycle. Ensures connections with the partner organization (i.e. GPS) to drive share, partner health, that aligns with execution plans to accelerate customer value realization at scale.
- Assesses and qualifies sales opportunities following sales frameworks and guidelines, ensuring alignment with AI\-enhanced sales methodologies and best practices. Assists in strategy development for pursuing and closing deals. Collaborates across organizations to contribute to deal orchestration and handoffs throughout the deal lifecycle with minimal guidance. Utilizes best practices to gain customer buy\-in, secure deals, and manage risks to support sales activities across the solution area.
Sales \& Pipeline Management:* Participates in sales pipeline reviews with internal stakeholders to drive forecasting accuracy and meeting sales targets, ensuring the use of AI\-powered analytics and forecasting tools to enhance precision. Maintains sales and/or consumption pipeline hygiene to enable tracking to achieve assigned sales metrics using all available tools, resources, and processes. Drives usage and/or consumption pipeline hygiene to actively monitor adoption trends, identify opportunities for intervention, enabling customers to realize the value of solutions purchased, drive expansion, and ensure healthier, more predictable renewals.
- Assesses business and emerging opportunities in context of provided feedback to contribute to the enhancement of the customer portfolio and support customer innovation initiatives. Utilizes technology (e.g., AI sales agents, automation, Power Platforms) to support growth within the assigned domain. Reviews propensity, renewal, consumption, and usage data to help inform sales strategy prioritization. Coordinates with partners assigned to each account and/or opportunity to assist in handoffs with other teams (e.g., Global Partner Solutions \[GPS], Customer Success Unit \[CSU], Industry Solutions Delivery \[ISD], Partner) throughout the deal lifecycle.
Sales Strategy:* Participates in discussions with stakeholders and decision makers for customers to identify and qualify sales opportunities. Collaborates with internal stakeholders to support and contribute to customer success. Collaborates with account teams to align the customer's artificial intelligence (AI) transformation vision with their business priorities and success objectives. Incorporates security principles in customer interactions, opportunity, and pursuits to maintain trust and compliance standards.
- Collaborates with others cross\-organizationally and assists in the development of solutions that facilitate AI\- and cloud\-driven transformations for customers within their solution area. Utilizes existing strategies to help differentiate Microsoft's offerings in a competitive landscape. Leverages established guidelines to advise customers on technologies and solutions that align with their goals and drive digital transformation.
- Leverages whitespace analysis and partners with others to identify business opportunities within the assigned market domain, utilizing AI\-driven market intelligence tools to assess trends and insights. Gathers and assesses market intelligence, trends, and insights with the team. Utilizes established market analysis approach to align sales strategies with market trends with minimal guidance.
- Assists in assessing solution area(s) and identifies trends to support aligning sales plays with customer demands, incorporating AI\-driven predictive analytics to forecast future market needs. Collaborates across teams to suggest solutions that support customer business objective
Other
- Embodies our Culture \& Values
Qualifications Required Qualifications
- Bachelor's Degree in Computer Science, Information Technology, Business Administration, or related field AND 2\+ years technology\-related sales or account management experience
+ OR equivalent experience
Other Requirements
- This position is not eligible for visa sponsorship. Candidates must have authorization to work in the United States that does not now or in the future require employer sponsorship.
Preferred Qualifications
- Master's Degree in Business Administration (i.e., MBA), Information Technology, Information Security, or related field AND 6\+ 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 8\+ years experience in technology\-related sales or account management
+ OR equivalent experience
- 8\+ years solution or services sales experience
- 8\+ years of experience selling business solutions to global customers with a focus on Cloud Native, AI, and/or Data Platform and Analytics
- 8\+ years of experience engaging with large enterprise customers, including managing multiple stakeholders and navigating complex sales cycles
- Working knowledge of Cloud Platform: Understanding of Microsoft Azure infrastructure, data, and AI application platforms, with the ability to articulate technical solutions to business and IT stakeholders
Solution Area Specialists IC3 \- The typical base pay range for this role across the U.S. is USD $88,000\.00 \- $150,100\.00 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 $114,500\.00 \- $165,700\.00 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
Solution Area Specialists IC4 \- The typical base pay range for this role across the U.S. is USD $107,600\.00 \- $187,500\.00 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\.00 \- $205,600\.00 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 $88K-$205K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 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 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($146K) sits 19% below the category median. Disclosed range: $88K to $205K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Microsoft AI Hiring
Microsoft has 16 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer, Data Scientist, AI Product Manager. Positions span Cambridge, MA, US, Redmond, WA, US, Mountain View, CA, US. Compensation range: $175K - $304K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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