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Overview
In addition, this role has people management responsibilities including driving employee growth and development, executing projects, and managing performance. Acts as a representative for technology domain to all C\-suite leaders within local market while engaging with and leveraging local Microsoft senior leadership as appropriate. Uses broad knowledge to build credibility with customers and is sought after for expertise. Enables and empowers team to influence customer decisions, and ensure technical wins by streamlining processes and managing the flow of wins, leveraging deep knowledge of processes. Leverages in\-depth knowledge of resources. Orchestrates team resources and coaches team to maximize impact of customer engagement and drive long\-term global strategy through global capacity planning, prioritization, and utilization of resources. Maximizes national/international\-level capacity and capabilities and influences future potential partner models by coaching team to grow partner network, identifying gaps, and promoting Microsoft within the Microsoft ecosystem. Supports partner technical capacity by monitoring and analyzing resources through interactions, communicating with managers, and identifying new partnership opportunities to build global strategy. Generates new compete strategies and builds competitive knowledge of team by enabling competitive learning and identifying experts to share knowledge. Coaches and provides support to team and across internal teams to define and execute strategy.
Responsibilities
Build Strategy
Generates new compete strategies and builds competitive knowledge of team by enabling competitive learning and identifying experts to share knowledge. Enables team to share compete knowledge with internal teams and global communities, influence compete strategies, and highlight Microsoft advantages during architecture and capability discussions. Acts as a subject matter expert on a particular competitive discipline(s).
Coaches and provides support to team and across internal teams to define and execute strategy. Drives repeatability of strategy. Proactively approaches customers to understand and identify global/cross\-area strategy opportunities, aligning strategy with industry insights. Engages internal teams to ensure capability to execute strategy.
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.
Works with global account and marketing teams to shape strategic win and customer success plans and tailor to audience for the global or area markets using knowledge of Microsoft offerings, their context in the competitive landscape, broader market trends, and in\-depth industry knowledge. Prepares teams for future market and opportunities. Ensures execution of compete strategy across teams, collaborating with cross\-functional groups as needed. Where applicable, directs teams in the building of consumption plans with complex requirements in coordination with Partner and Industry Solutions Delivery teams after customer sign\-off.
Education
Ensures team members participate in tech communities and drives feedback to improve overall team member experience and effectiveness at global level.
Acts as a technical thought leader by sharing best practices (e.g., architectures, materials) and regularly delivering content at Microsoft events (e.g., TechReady). Provides insight into how to identify and win opportunities to increase solutions/portfolio understanding and capabilities, emphasizing continuous security enhancement and regulatory compliance.
Enables and empowers team to develop technical expertise and provide technical insights to internal teams. Acts as a role model by increasing own technical knowledge and serving as a respected technology leader to broader organization. Provides insight onto Corporate, business and product groups, sales strategy, and business reviews for impact.
Leverage Partner Ecosystem
Maximizes national/international\-level capacity and capabilities and influences future potential partner models by coaching team to grow partner network, identifying gaps, and promoting Microsoft within the Microsoft ecosystem (e.g., account teams). Raises escalations or alleviates blockers through collaboration with manager\-level counterparts in cross\-functional groups.
Supports partner technical capacity by monitoring and analyzing resources through interactions, communicating with managers, and identifying new partnership opportunities to build global strategy.
People Management
Managers deliver success through empowerment and accountability by modeling, coaching, and caring. Model: Live our culture. Embody our values. Practice our leadership principles. Coach: Define team objectives and outcomes. Enable success across boundaries. Help the team adapt and learn. Care: Attract and retain great people. Know each individual’s capabilities and aspirations. Invest in the growth of others.
Scale Customer Engagements
Leverages in\-depth knowledge of resources (e.g., roles, Microsoft Technology Center \[MTC], demo sites, virtual sites, Value Based Delivery \[VBD], Customer Success Unit \[CSU]) and owns resolution of highly complex, escalated technical and non\-technical blockers by engaging other teams (e.g., engineering, Account Team Unit \[ATU], Specialist Team Unit \[STU]) and conveying impact. Leverages insights and knowledge of blockers to anticipate market needs and contribute to global strategy. Identifies patterns of blockers within area of expertise and reaches out across geos to consolidate patterns into a business case to accelerate escalation.
Acts as a representative for technology domain to all C\-suite leaders within local market while engaging with and leveraging local Microsoft senior leadership as appropriate. Coaches and enables team to identify and proactively engage with key customer technical decision makers and influencers to unblock technical and business obstacles.
Enables and empowers team to influence customer decisions, and ensure technical wins by streamlining processes and managing the flow of wins, leveraging deep knowledge of processes (e.g., Managed Service Provider \[MSP], Managed Certified Professional \[MCP]), tools, and programs (e.g., FastTrack, End Customer Investment Funds). Aligns goals across workloads and facilitates cross\-selling with various worldwide teams.
Orchestrates team resources and coaches team to maximize impact of customer engagement and drive long\-term global strategy through global capacity planning, prioritization, and utilization of resources. Proactively plans team resources and engages corporate teams to influence maturation and presales. Qualifies and prioritizes opportunities, and holds team accountable for maximizing selling time to achieve scorecard objectives and subsidiary strategy.
Uses broad, in\-depth industry, technical, and professional knowledge, and leadership skills to build credibility with customers and act as a trusted advisor to assess and consult on their security needs, and is sought after for expertise.
Ensures consistency and quality through capturing, sharing, and adherence of standards and best practices in customer engagements by scaling implementation of cross\-functional initiatives across channels at the global level to drive consistency in technical approach and ensure customer technical experience across teams.
Solution Design and Proof
Coaches team to envision new and innovative solutions that use Microsoft technology to meet customer needs. Supports team in coordinating with other stakeholders to develop solutions for complex sales scenarios. Ensures subsidiary level capabilities to execute on those solutions.
Applies advanced sales methodologies (e.g., challenger sales) and coaches team to lead and inspire global customers in digital transformation solutions, emphasizing security improvements, and shaping and enabling scalable change across subsidiaries.
Promotes leveraging of reference architectures and provides validation input to establish programmatic frameworks for use by the business.
Oversees highly complex demonstrations (e.g., architectural design sessions, and proof of concept \[POC] sessions, pilots, hackathons) of end\-to\-end Microsoft solutions and architectures based on multiple products and positions solutions against competitors. Ensures demonstrations enable customers to identify and resolve technical issues with clear criteria and next steps that guarantee deployment of Microsoft technology.
Identifies new technical and business trends and needs, and identifies ideas that can be transformed into solutions that benefit customers/partners, serving as the voice of the customer (VOC) to Microsoft. Ensures input is utilized and solutions for the customer are established at the worldwide level.
Qualifications Required/minimum qualifications:
Master's Degree in Computer Science, Information Technology, Business or related field AND 6\+ years technical pre\-sales or technical consulting experience OR Bachelor's Degree in Computer Science, Information Technology, or related field AND 8\+ years technical pre\-sales or technical consulting experience OR 10\+ years technical pre\-sales or technical consulting 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.*
Additional or preferred qualifications:
10\+ years technical pre\-sales, technical consulting, or technology delivery, or related experience OR equivalent experience.
8\+ years experience with cloud and hybrid, or on premises infrastructures, architecture designs, migrations, industry standards, and/or technology management.
5\+ 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 M6 \- 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
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 $155K-$303K range is above the 75th percentile 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($229K) sits 5% above the category median. Disclosed range: $155K to $303K.
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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