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About This Role
Overview
Security represents the most critical priorities for our customers in a world awash in digital threats, regulatory scrutiny, and estate complexity. Microsoft Security aspires to make the world a safer place for all. We want to reshape security and empower every user, customer, and developer with a security cloud that protects them with end-to-end, simplified solutions. The Microsoft Security organization accelerates Microsoft's mission and bold ambitions to ensure that our company and industry is securing digital technology platforms, devices, and clouds in our customers' environments, as well as ensuring the security of our own internal estate. Our culture is centered on embracing a growth mindset, a theme of inspiring excellence, and encouraging teams to bring their best each day. In doing so, we create life-changing innovations that impact billions of lives around the world.
Our team turns real vulnerabilities and adversary behavior into reproducible tasks that models and agents can learn from and be evaluated against. Reliable grading and reward signals are central to this work: success must reflect a verified security outcome, not simply a convincing response.
We are looking for several Principal and Senior Security Research Engineers to build realistic, safely contained attack scenarios across cloud, on-premises, and hybrid environments. You will combine hands-on security research with software engineering to reproduce vulnerable conditions, construct representative multi-step attack chains, and implement checks that establish what actually happened. Your scenarios will support reinforcement learning for Microsoft’s MAI family of models and evaluation of security Perception agents and the software harnesses that run them. Working with threat researchers, AI engineers, and environment engineers, you will test whether systems can distinguish threats from benign activity, connect evidence across an attack chain, and identify effective responses. You will own scenarios from research through implementation and grading, adapting them as attacker techniques evolve. Your work will give teams trustworthy training signals and concrete evidence of whether model, agent, or harness changes improve security capabilities that matter to customers.
Candidates residing within commuting distance of the Redmond or Reston hub are required to work on-site under a hybrid work arrangement, in accordance with Microsoft’s and the team’s in-office requirements for these designated locations.
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* Turn threat research into working scenarios, reproducing real vulnerabilities and emerging adversary techniques in cloud, on-premises, and hybrid environments.
- Build realistic attack chains.
- Connect vulnerable applications, identity weaknesses, and misconfigurations into multi-step scenarios with verified prerequisites and observable security impact.
- Make outcomes verifiable.
- Build automated graders and reward signals grounded in system state and telemetry, distinguishing agent success, effective defenses, and environment failures.
- Evaluate beyond familiar patterns.
- Create attack variants, benign lookalikes, and patched controls that expose missed threats, false positives, and memorization.
- Deliver safe, reusable research.
- Own scenario code, isolation, evidence capture and reset; partner with AI and environment engineers to turn findings into better training and evaluation. 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.
- 5+ years of experience researching vulnerabilities, conducting authorized offensive security tests, investigating security incidents, or developing security tools.
- 3+ years of experience developing and debugging security tools or scenario automation in Python, C#, Go, C/C++, or TypeScript, using version control and automated tests.
- Experience independently reproducing a vulnerability or adversary technique and documenting its prerequisites, reproduction steps, and observed security impact.
- Experience building or validating multi-step attack chains across applications, identities, or hosts, including the access requirements and evidence for each step.
- Experience assessing cloud IAM in Azure, AWS, or GCP and testing authentication, permissions, or network access in Windows or Linux environments.
- Experience confirming or rejecting security findings through source-code review, telemetry, or controlled tests, including checking whether existing safeguards prevent the claimed impact.
- Experience running authorized security tests with isolated targets, lab-only credentials, execution limits, and cleanup procedures.
- Reconstructed attacks from incident evidence or threat reports for red-team, purple-team, or adversary-emulation exercises.
- Reproduced attack paths across cloud and on-premises identity systems involving federation, service principals, workload identities, or directory services.
- Used source-code analysis, debugging, or reverse engineering to identify a vulnerability's root cause and verify a fix.
- Built automated graders or reward functions that check system state and telemetry, including checks that distinguish scenario failures from agent failures.
- Created attack variants, benign comparison cases, patched scenarios, or simulated user activity to test detection accuracy and false positives.
#MSFTSecurity #Cybersecurity #SecurityResearch #AgentSecurity #AgenticEvals #Perception
Security Research IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 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 $160,200 - $261,000 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
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 $119K-$304K range is above the median for Research Engineer roles in our dataset (median: $192K across 50 roles with salary data).
View full Research Engineer salary data →Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 9,653 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Microsoft, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $252,000 based on 419 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($212K) sits 16% below the category median. Disclosed range: $119K to $304K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and AI Engineering Manager ($251,622). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Microsoft AI Hiring
Microsoft has 69 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer, AI Software Engineer, AI Product Manager. Positions span Washington, DC, US, Mountain View, CA, US, TX, US. Compensation range: $143K - $331K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national median.
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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