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About This Role
Overview
Security is one of the most critical priorities for our customers in a world of growing digital threats, regulatory scrutiny, and estate complexity. Microsoft Security aspires to make the world safer by empowering every user, customer, and developer with a security cloud that delivers end\-to\-end, simplified protection. The Microsoft Security organization advances this mission by helping secure digital technology platforms, devices, and clouds across customers' heterogeneous environments, while also protecting Microsoft's internal estate. Our culture is grounded in a growth mindset, inspiring excellence, and enabling teams and leaders to bring their full potential each day.
The Microsoft Security Research (MSecR) Purple Team sits at the intersection of offense, defense, and intelligence. AI is now everywhere: organizations and people across every industry are adopting large language models, copilots, and autonomous agents, and these systems bring a brand\-new attack surface that can be manipulated, misused, and exploited in ways traditional software cannot. In Microsoft Security, we protect AI from these attacks. We are looking for a Principal\-level security researcher who combines a real security background with a deep, hands\-on understanding of how modern AI works and how it breaks. You will help execute and conduct AI attacks through purple team simulations, using what you find to make AI safer and to inform our security products so they protect customers better.
This role is for someone who wants to shape how AI attacks are simulated and defended at scale. You will set methodology, build tooling, influence product direction, and drive innovation in attacking and defending AI systems (Security for AI).
Responsibilities
As a Principal Security Researcher, you will:
- Lead the design and execution of purple team simulations aimed at making AI safer, conducting realistic AI attacks whose findings inform our security products and help them protect customers more effectively.
- Develop and execute hands\-on attacks against AI systems, including:
- Direct prompt injection and jailbreaks that bypass system prompts, safety instructions, and alignment.
- Indirect (cross\-domain) prompt injection (XPIA), where malicious instructions are hidden in untrusted content the model consumes: documents, web pages, emails, tickets, code, and RAG sources.
- Multi\-turn, crescendo, and social\-engineering style attacks that steer a model toward unsafe behavior over a conversation.
- Multimodal injection through images, audio, and files that carry hidden instructions.
- Guardrail, content filter, and safety\-system bypass, including encoding, obfuscation, and adversarial phrasing.
- Sensitive data exfiltration and leakage, including system prompt and grounding\-data disclosure and cross\-tenant or cross\-session leakage.
- Agentic and tool\-use abuse: excessive agency, confused\-deputy attacks, unsafe autonomous actions, and manipulation of tools, plugins, and connected systems.
- Data and model attacks: training\-data and RAG poisoning, model extraction, model inversion, membership inference, and adversarial examples / evasion.
- Build scalable AI red teaming tooling and automation (custom attack harnesses and evaluation frameworks) to generate attack variations, run large evaluations, and continuously test AI systems as they change.
- Partner with product, engineering, Responsible AI, and detection teams to translate findings into mitigations: better system prompts, input and output filters, grounding controls, agent guardrails, and detections.
- Evaluate the effectiveness of AI defenses (detections, safety classifiers, filters, and monitoring) and provide strategic recommendations to close gaps.
- Conduct deep research into emerging AI attacker techniques and map them to frameworks such as MITRE ATLAS and the OWASP Top 10 for LLM Applications.
- Deliver executive\-level briefings, technical reports, and prioritized, actionable recommendations.
- Act as a technical leader: shape AI simulation methodology, mentor team members, and drive long\-term innovation in Security for AI.
Qualifications Minimum Qualifications:
- Doctorate in Statistics, Mathematics, Computer Science, Computer Security, or related field AND 3\+ years experience in software development lifecycle, large\-scale computing, threat analysis or modeling, cybersecurity, vulnerability research, and/or anomaly detection.
- + OR Master's Degree in Statistics, Mathematics, Computer Science, Computer Security, or related field AND 4\+ years experience in software development lifecycle, large\-scale computing, threat analysis or modeling, cybersecurity, vulnerability research, and/or anomaly detection.
+ OR Bachelor's Degree in Statistics, Mathematics, Computer Science, Computer Security, or related field AND 6\+ years experience in software development lifecycle, large\-scale computing, threat analysis or modeling, cybersecurity, vulnerability research, and/or anomaly detection.
+ OR equivalent experience.
Other Requirements:
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include, but are not limited to the following specialized security screenings:
Microsoft Cloud Background Check:
- This position will be required to pass the Microsoft background and Microsoft Cloud background check upon hire/transfer and every two years thereafter.
Preferred Qualifications:
- Doctorate in Statistics, Mathematics, Computer Science, Computer Security, or related field AND 5\+ years experience in software development lifecycle, large\-scale computing, threat analysis or modeling, cybersecurity, vulnerability research, and/or anomaly detection.
- + OR Master's Degree in Statistics, Mathematics, Computer Science, Computer Security, or related field AND 8\+ years experience in software development lifecycle, large\-scale computing, threat analysis or modeling, cybersecurity, vulnerability research, and/or anomaly detection.
+ OR Bachelor's Degree in Statistics, Mathematics, Computer Science, Computer Security, or related field AND 12\+ years experience in software development lifecycle, large\-scale computing, threat analysis or modeling, cybersecurity, vulnerability research, and/or anomaly detection.
+ OR equivalent experience.
- A security background: experience finding and exploiting real vulnerabilities (for example penetration testing, red teaming, vulnerability research, or application security), with an adversarial mindset.
- Solid, practical understanding of how modern AI works: LLMs, prompting, retrieval\-augmented generation (RAG), fine\-tuning, and agentic / tool\-using systems.
- Hands\-on experience and familiarity with agentic AI systems, red teaming harnesses, and AI tooling: building or working with agents, tool\-using LLMs, orchestration and evaluation frameworks, LLM APIs, and attack harnesses.
- Demonstrated hands\-on experience attacking AI systems: prompt injection (direct and indirect), jailbreaks, guardrail bypass, data exfiltration, or agent abuse, in research or professional settings.
- Python skills for building attack automation, evaluation harnesses, and research tooling.
- Familiarity with AI security and safety frameworks such as MITRE ATLAS, OWASP Top 10 for LLMs and Responsible AI principles.
- Ability to translate offensive findings into practical defenses and to communicate clearly with both engineers and executives.
- Contributions to AI security research, publications, CTFs, or open\-source AI red teaming tooling.
\#MSsecurity
Security Research IC5 \- The typical base pay range for this role across the U.S. is USD $142,800 \- $274,800 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 $188,000 \- $304,200 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 $142K-$304K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $142K to $304K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Microsoft AI Hiring
Microsoft has 42 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager, Data Scientist. Positions span US, CA, US, Redmond, WA, US. Compensation range: $147K - $331K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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