Interested in this AI/ML Engineer role at Microsoft?
Apply Now →Skills & Technologies
About This Role
Overview
Are you passionate about AI and eager to transform the IT landscape? Join our dynamic team as a Senior 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 Senior 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.
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:
+ Assesses and qualifies sales opportunities following sales frameworks and guidelines, ensuring alignment with AI\-enhanced sales methodologies and best practices. Participates in strategy development for driving and closing moderately complex, significant value opportunities. Collaborates across organizations (e.g., Account Team Unit \[ATU], CSU, ISD, GPS) to contribute to deal orchestration and handoffs throughout the deal lifecycle. Utilizes best practices to earn customer buy\-in, secure deals, and mitigate risks to facilitate sales activities across the solution area.
+ Drives sales strategies tailored to each customer's security priorities, showcasing Microsoft's dedication to secure, AI\-powered transformation and addressing their specific needs within the customer success plan. Reviews and translates customer and partner feedback to establish recovery action plans. Develops relationships with key customer and partner stakeholders to understand sentiment and satisfaction goals Drives partner teams, resources and relationships are activated, driving co\-selling strategies and partner attach to each opportunity through every stage in the sales lifecycle. Drives partner organization connections (i.e. GPS) to drive share, partner health, that aligns with execution plans to accelerate customer value realization at scale.
Sales \& Pipeline Management:
+ Analyzes business and emerging opportunities to improve the customer portfolio and encourage customer innovation. Leverages technology (e.g., AI sales agents, automation, Power Platforms) to drive growth within assigned domain. Utilizes propensity, renewal, consumption, and usage data to prioritize sales strategy. Optimizes partners assigned to each account and/or opportunity to facilitate handoffs with other teams (e.g., Global Partner Solutions \[GPS], Customer Success Unit \[CSU], Industry Solutions Delivery \[ISD], Partner) throughout the deal lifecycle.
+ Engages 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 Leads 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
Sales Strategy:
+ Evaluates solution area(s) and market and develops insights to align sales plays with customer business priorities and outcomes, incorporating AI\-driven predictive analytics to forecast future market needs. Partners across teams to propose solutions and strategies that align with customer business objectives. Identifies and addresses gaps, driving market engagement and sales execution.
+ Partners with others cross\-organizationally and supports the development of solutions to enable AI\- and cloud\-driven transformations for existing and new customers within the region, emphasizing the integration of cutting\-edge AI technologies and cloud services. Leverages established strategies and participates in conversations with customers to distinguish Microsoft’s offerings in the competitive landscape. Serves as an trusted advisor for customers and guides the adoption of technologies and solutions that align with their goals and drive digital transformation.
+ Leverages whitespace analysis and partners with others to identify business opportunities and market gaps within the assigned market domain, utilizing AI\-driven market intelligence tools to assess trends and insights. Evaluates and disseminates market intelligence, trends, and insights across the team. Leverages established market analysis approach to ensure alignment with strategic directives and market trends.
+ Leads discussions with customer stakeholders and decision makers to identify, qualify, prioritize and accelerate sales opportunities. Acts as a point of contact and collaborates with internal stakeholders within and across organizations to drive to customer success. Engages with account teams to align the customer's artificial intelligence (AI) transformation vision with their business priorities and success objectives. Incorporates and reinforces security principles in customer interactions, opportunity, and pursuits to maintain trust and compliance standards.Other:
+ Embody our culture and values
Qualifications Required Qualifications
- Bachelor's Degree in Computer Science, Information Technology, Business Administration, or related field AND 4\+ years of 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 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
- 12\+ years solution or services sales experience
- 12\+ years of experience selling business solutions to global customers with a focus on Cloud Native, AI, or Data platform and/or analytics
- 12\+ 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 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
Solution Area Specialists IC5 \- The typical base pay range for this role across the U.S. is USD $133,000 \- $222,700 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 $170,300 \- $239,800 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-$239K 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 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 $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 ($173K) sits 21% below the category median. Disclosed range: $107K to $239K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.