Interested in this AI/ML Engineer role at Microsoft?
Apply Now →Skills & Technologies
About This Role
Overview
Are you insatiably curious, deeply passionate about the realm of databases and analytics, and ready to tackle complex challenges in a dynamic environment in the era of AI? If so, we invite you to join our team as a Solution Engineer \- Cloud \& AI Data Platform in Innovative Data Platform for commercial customers at Microsoft. Here, you'll be at the forefront of innovation, working on cutting\-edge projects that leverage the latest technologies to drive meaningful impact. Join us and be part of a team that thrives on collaboration, creativity, and continuous learning.
Databases \& Analytics is a growth opportunity for Microsoft Azure, as well as its partners and customers. It includes a rich portfolio of products including IaaS and PaaS services on the Azure Platform in the age of AI. These technologies empower customers to build, deploy, and manage database and analytics applications in a cloud\-native way.
As an Solution Engineer \- Cloud \& AI Data Platform, you will play a pivotal role in helping enterprises unlock the full potential of Microsoft’s cloud database and analytics stack across every stage of deployment. You’ll collaborate closely with engineering leaders and platform teams to accelerate the Fabric Data Platform, including Azure Databases and Analytics, through hands\-on engagements like Proof of Concepts, hackathons, and architecture workshops. This opportunity will allow you to accelerate your career growth, develop deep business acumen, hone your technical skills, and become adept at solution design and deployment. You’ll guide customers through secure, scalable solution design, influence technical decisions, and accelerate database and analytics migration into their deployment workflows. In summary, you’ll help customers modernize their data platform and realize the full value of Microsoft’s platform, all while enjoying flexible work opportunities.
As a trusted technical advisor, you’ll guide customers through secure, scalable solution design, influence technical decisions, and accelerate database and analytics migration into their deployment workflows. In summary, you’ll help customers modernize their data platform and realize the full value of Microsoft’s platform.
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
- Drive technical sales with decision makers using demos and PoCs to influence solution design and enable production deployments.
- Lead hands\-on engagements—hackathons and architecture workshops—to accelerate adoption of Microsoft’s cloud platforms.
- Build trusted relationships with platform leads, co\-designing secure, scalable architectures and solutions.
- Resolve technical blockers and objections, collaborating with engineering to share insights and improve products.
- Maintain deep knowledge in Analytics Portfolio: Microsoft Fabric (OneLake, DW, real\-time intelligence, BI, Copilot), Azure Databricks, Purview Data Governance and Azure Databases: SQL DB, Cosmos DB, PostgreSQL.
- Maintain and grow knowledge in on\-prem EDW (Teradata, Netezza, Exadata), Hadoop \& BI solutions.
- Represent Microsoft through thought leadership in cloud Database \& Analytics communities and customer forums.
- Embody our culture and values
Qualifications Required Qualifications:
- 3\+ years technical pre\-sales or technical consulting experience, OR Bachelor's Degree in Computer Science, Information Technology, or related field AND 2\+ years technical pre\-sales or technical consulting experience OR Master's Degree in Computer Science, Information Technology, or related field AND 1\+ year(s) 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.
Preferred Qualifications:
- 5\+ years technical pre\-sales, technical consulting, or technology delivery, or related experience OR equivalent experience
- 3\+ years experience with cloud and hybrid, or on premises infrastructure, architecture designs, migrations, industry standards, and/or technology management
- Experience in data warehouse \& big data migration including on\-prem appliance (Teradata, Netezza, Oracle), Hadoop (Cloudera, Hortonworks) and Azure Synapse Gen2
Solution Engineering IC3 \- The typical base pay range for this role across the U.S. is USD $86,100 \- $169,800 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 $113,300 \- $187,400 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 Engineering IC4 \- The typical base pay range for this role across the U.S. is USD $106,400 \- $203,600 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 $137,600 \- $222,600 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 $86K-$222K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($154K) sits 17% below the category median. Disclosed range: $86K to $222K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Microsoft AI Hiring
Microsoft has 20 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Research Scientist, Data Scientist. Positions span New York, NY, US, Mountain View, CA, US, San Francisco, CA, US. Compensation range: $151K - $331K.
Location Context
AI roles in Austin pay a median of $215,300 across 535 tracked positions. That's 7% 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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.