Interested in this AI/ML Engineer role at Microsoft?
Apply Now →Skills & Technologies
About This Role
Overview
Microsoft’s mission is to empower every person and every organization on the planet to achieve more. The Specialist Sales Manager – Cloud \& AI Platforms (CAIP) leads a high‑impact team responsible for driving Azure adoption and AI\-powered transformation for Majors Enterprise High Tech and Process Manufacturing customers in the Manufacturing \& Mobility operating unit.
This is a quota\-carrying role with direct quota accountability for performance outcomes across the territory. The manager leads a team of 10–12 quota‑carrying Cloud \& AI sales specialists, and is accountable for the team’s execution, forecast integrity, pipeline health, and attainment across core Azure solution plays. The role is a portfolio leadership position requiring executive engagement, multi‑workload strategy leadership, and cross‑functional orchestration across Account Teams, Solution Engineering, Customer Success, partners, and corporate resources.
Success requires an outcomes\-first approach grounded in the core industry motions shaping High Tech and Process Manufacturing: Modernize SAP, Data \+ AI platform modernization, Supply Chain transformation, Product Design / Digital Engineering, Factory/Plant modernization (Intelligent Factory), and security and governance across IT/OT environments.
Responsibilities
People Leadership \& Team Performance (Quota\-Carrying Team)
- Lead, coach, and develop a team of 10–12 quota‑carrying Cloud \& AI Platform sales specialists to deliver customer outcomes and meet or exceed quota across Infrastructure, Data, AI, App Modernization, Developer, and Security.
- Establish a high‑performance culture focused on accountability, disciplined execution, pipeline rigor, forecasting accuracy, and consistent deal governance tied to quota attainment.
- Drive coaching and operational cadence (pipeline reviews, deal inspections, forecast calls, business rhythm) to improve conversion, quality, and predictability.
- Set clear expectations for seller execution, including opportunity ownership, milestone hygiene, and cross\-role orchestration required to drive consistent performance.
Industry Portfolio Strategy – High Tech \& Process Manufacturing (Majors Enterprise)
- Own and execute an industry\-specific Azure strategy across a portfolio of Majors Enterprise High Tech \& Process Manufacturing customers, prioritizing the highest\-impact opportunities that drive quota attainment.
- Lead the team to align customer priorities to Microsoft solution plays and translate strategy into actionable pursuits tied to measurable business outcomes.
- Drive the territory strategy around the highest\-impact motions, including:
+ Modernize SAP and adjacent enterprise applications, including migration, modernization, and enterprise integration patterns.
+ Data estate modernization and AI platform adoption, including governance, analytics modernization, and scalable AI enablement.
+ Supply Chain outcomes, including visibility, agility, planning, and risk management scenarios.
+ Product Design / Digital Engineering outcomes, including engineering data platforms, simulation/HPC readiness, and digital thread enablement.Factory/Plant modernization (Intelligent Factory), including IT/OT data integration, digital twins, production optimization, quality/maintenance scenarios, edge/plant infrastructure modernization, and OT security.
+ Security, compliance, and governance expectations typical of large industrial customers.
Sales, Forecasting, and Quota Accountability
- Carry a quota and maintain direct accountability for quota attainment, forecast accuracy, and execution outcomes across the portfolio.
- Maintain accountability for forecast integrity, pipeline health, and risk management; ensure the team runs disciplined inspection and maintains accurate reporting.
- Drive opportunity identification, qualification, and closure across Azure solution plays, ensuring consistent progression, milestone hygiene, and clear close plans.
- Lead strategic deal shaping across v‑teams to land integrated solutions that combine cloud foundation, data, AI, application modernization, and security.
Customer \& Executive Engagement
- Build trusted relationships with senior customer stakeholders (CIO/CTO/CDO/COO and business leaders across operations, supply chain, engineering, and finance) and position Microsoft as a strategic partner for transformation.
- Serve as an escalation point for complex pursuits and customer situations requiring executive presence, negotiation leadership, governance, and cross‑company alignment.
- Lead executive conversations that connect Azure capabilities to industry outcomes, accelerate decision\-making, and increase close confidence.
Cross‑Functional Orchestration \& Partner Ecosystem
- Orchestrate execution with Account Teams, Solution Engineering, Customer Success, partners, deal desk, and other specialist teams to deliver “One Microsoft” outcomes for customers.
- Activate and leverage partners aligned to High Tech and Process Manufacturing motions to scale delivery and accelerate time‑to‑value.
- Ensure internal alignment across stakeholders and drive resolution of blockers that impact execution speed, customer commitments, adoption, and attainment.
Qualifications Required/minimum qualifications
- Bachelor's Degree in Computer Science, Information Technology, Business Administration, Information Security, or related field AND 6\+ years experience in technology\-related sales or account management 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
- 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.
- 3\+ years people management experience.
- Experience selling to or leading enterprise pursuits in High Tech, Industrial Manufacturing, Chemicals, Metals, Packaging, or broader Process Manufacturing segments.
- Familiarity with the customer and solution patterns common in this space: SAP modernization, data platform modernization, AI transformation, supply chain outcomes, product design/digital engineering, factory/plant modernization, and IT/OT security considerations.
- Track record of cross‑functional orchestration and partner\-led execution in complex enterprise environments.
- Executive presence with ability to drive governance, align stakeholders, and close high‑complexity deals.
Solution Area Specialists M5 \- 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 $133K-$239K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($186K) sits 12% above the category median. Disclosed range: $133K to $239K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Microsoft AI Hiring
Microsoft has 49 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager, Data Scientist. Positions span Redmond, WA, US, San Francisco, CA, US, Washington, DC, US. Compensation range: $159K - $331K.
Location Context
AI roles in San Francisco pay a median of $244,000 across 1,059 tracked positions. That's 33% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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.