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About This Role
Overview
Microsoft’s Commercial Solution Area (CSA) plays a central role in shaping sales strategy, leading technical sales, and driving operations that power the Microsoft Cloud. As an Americas Solution Engineer Manager within the enterprise sales organization, you will lead and develop a high\-performing team of Solution Engineers across Azure Infrastructure, Data \& AI, and App Innovation. This role is critical to executing Microsoft’s Azure strategy, ensuring coordinated efforts across cloud, data, application, and security solutions. You and your team will serve as trusted advisors to customers, driving technical decisions that deliver meaningful business outcomes and enable long\-term growth for Microsoft.
In this role, you will be responsible for building and managing a strong, diverse team while fostering a culture of continuous learning, accountability, and performance excellence. You will actively engage with customer technical decision\-makers and key influencers, partnering closely with sales teams to position solutions and accelerate outcomes. By empowering your team with the right tools, processes, and coaching, you will enable them to effectively influence customer decisions, secure technical wins, and deliver consistent, high\-quality engagement experiences across the sales lifecycle.
You will also drive operational excellence by orchestrating team resources and ensuring effective prioritization, capacity planning, and cross\-workload alignment to maximize impact. This includes promoting best practices, standardizing engagement approaches, and leading cross\-functional initiatives that enhance consistency and scale across the organization. Additionally, you will strengthen ecosystem collaboration by expanding partner engagement and identifying capability gaps, while coaching your team and internal stakeholders to execute on mid\- to long\-term strategy and deliver sustained business results.
Responsibilities
People Management: Develops a high\-performing team by hiring diverse talent, prioritizing a healthy culture and development, leading by example and by preparing people for more upper\-level positions in other parts of the organization.
Coaches Seller’s with a “challenger mentality” by prompting Seller’s to engage early and lead with new insights on how to grow the customers’ business.
Build Strategy
Builds competitive knowledge of the team by enabling competitive learning and identifying experts to share knowledge. Enables team to share compete knowledge with internal teams and communities, influence compete strategies and highlight Microsoft advantages during architecture and capability discussions.
Maintains communications with internal partners (e.g., Account Technology Unit \[ATU], Customer Service Unit \[CSU] manager, SSM) on highest potential customers to pre\-align technical resources to customer and customer cases based on account planning and priorities, with the flexibility to realign to minimize orchestration and enable proactive engagements as needed.
Coaches and provide support to team and across internal teams to define and execute strategy. Engages internal teams to ensure capability to execute strategy.
Works with local marketing and account teams to shape strategic win and customer success plans and tailor to audience for the local markets using knowledge of Microsoft offerings, their context in the competitive landscape, and broader market trends. Ensures the team is equipped to execute compete strategy, collaborating with cross\-functional groups as needed.
Pipeline and Sales Management: Lead sellers to build and maintain accurate pipeline and drive the Azure business to overachieve revenue, consumption, and scorecard targets.
Drives partnership with sales teams and other teams engaging in the customer (e.g., partners, services, Customer Success, engineering support, etc.) to identify new business opportunities
Customer Centricity: Lead from the front, personally engaging at Executive level to support and coach teams on opportunity discovery and acceleration Engages with technical and business leaders on both business and technical outcomes to drive customer value.
Partner Engagement: Bring together Microsoft solutions with Partner solutions, fully leverage the synergy effect with Partner solutions, fully leverage the synergy effect with our partners, and co\-sell with them to make deals bigger and faster. Supports partner technical capacity by monitoring and analyzing resources through interactions, communicating with managers, and identifying new partnership opportunities to build subsidiary strategy.
Other: Embody our culture and values
Qualifications Required/minimum qualifications
Master's Degree in Computer Science, Information Technology, Business or related field AND 4\+ years technical pre\-sales or technical consulting experience OR Bachelor's Degree in Computer Science, Information Technology, or related field AND 6\+ years technical pre\-sales or technical consulting experience OR 7\+ years technical pre\-sales or technical consulting experience OR equivalent experience.
Other Qualifications :*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.*
Additional or preferred qualifications
8\+ years technical pre\-sales, technical consulting, or technology delivery, or related experience OR equivalent experience.
6\+ years experience with cloud and hybrid, or on premises infrastructures, architecture designs, migrations, industry standards, and/or technology management.
3\+ years people management experience (including leading virtual teams).
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 and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.
Solution Engineering M5 \- 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
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 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($201K) sits 8% below the category median. Disclosed range: $130K to $272K.
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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