Interested in this AI/ML Engineer role at CrowdStrike?
Apply Now →About This Role
As a global leader in cybersecurity, CrowdStrike protects the people, processes and technologies that drive modern organizations. Since 2011, our mission hasn’t changed — we’re here to stop breaches, and we’ve redefined modern security with the world’s most advanced AI-native platform. We work on large scale distributed systems, processing almost 3 trillion events per day and this traffic is growing daily. Our customers span all industries, and they count on CrowdStrike to keep their businesses running, their communities safe and their lives moving forward. We're proud to work for a mission-driven company leveraging AI to transform the way we work. CrowdStrikers drive their careers through flexibility and autonomy while also being expected to contribute to a culture of responsible AI adoption, experimentation, and innovation. We use an AI-first mindset as a force multiplier to proactively and continuously accelerate execution, build expertise, uncover insights, and solve complex problems. We’re always looking to add talented CrowdStrikers to the team who have limitless passion, a relentless focus on innovation and a fanatical commitment to our customers, our community and each other. Ready to join a mission that matters? The future of cybersecurity starts with you.
About the Role:
CrowdStrike is seeking a Distinguished Engineer to research, define, and build the future of cybersecurity AI within our Cybersecurity Superintelligence Lab. This role brings together frontier AI research, deep cybersecurity expertise, and large-scale systems engineering. The mandate is broad: understand where AI is going, identify new approaches to cybersecurity, and build systems that fundamentally improve how defenders operate.
You will build against what is possible today while anticipating what advances in models and agentic systems could make possible over the next 12-24 months. That means thinking about the entire application and the systems behind it, from models and agents to security data, tools, and execution environments. The work may span autonomous investigation, threat hunting, adversary simulation, detection, response, and remediation. Some of the most important use cases have yet to be defined.
As a Distinguished Engineer, you will be one of the senior technical leaders of the Lab. You will make original technical contributions while setting direction for teams working on these problems. You will identify important research questions, develop new approaches, build prototypes, establish rigorous evaluations, and help turn successful research into capabilities that operate at CrowdStrike scale.
What You'll Do:
- Define the future of cybersecurity AI. Track advances at the frontier of AI and determine what they make possible for cybersecurity. Identify new capabilities and use cases early, with a particular focus on transforming how defenders operate. Set the research directions and technical approaches required to pursue them.
- Invent and build new cybersecurity AI systems. Design end-to-end applications that reason across security context, use tools, coordinate, and execute complex security tasks. Determine how models, agents, security telemetry, and execution environments should work together to enable new capabilities.
- Develop systems that improve through experience. Use investigations, adversary behavior, simulations, and expert feedback to improve system behavior. Explore how offensive validation and defensive outcomes can generate better evidence, evaluations, and training signals. Work directly with threat researchers, incident responders, red teamers, and other security experts to keep the research grounded in real-world defense.
- Establish rigorous evaluations. Build benchmarks, cyber environments, and evaluation harnesses that expose capabilities and failure modes. Measure security efficacy, reliability, performance, and cost.
- Set technical direction across the Lab. Lead consequential research and architecture decisions across teams. Work directly on hard technical problems, mentor senior researchers and engineers, and drive alignment through prototypes, experiments, and evidence.
- Turn research breakthroughs into real systems. Carry promising work beyond demonstrations and solve the technical problems required to operate against high-volume security data and adaptive adversaries at CrowdStrike scale.
- Contribute to the broader technical community. Represent CrowdStrike through research publications, open-source contributions, technical collaborations, and engagement with the AI and cybersecurity research communities.
What You'll Need:
- PhD in Computer Science, Computer Engineering, Applied Mathematics, Statistics, Neuroscience, or a related quantitative field.
- A sustained record of original work at the intersection of AI and cybersecurity, including research or systems that introduced new capabilities, approaches, or applications.
- Deep cybersecurity expertise developed through significant technical work in areas such as threat hunting, detection and response, vulnerability research, adversary behavior, security analytics, or security automation. You can independently identify important security problems and opportunities for applying AI to them.
- Deep hands-on experience architecting and building end-to-end AI systems that combine models, reasoning, tools, data, and execution environments.
- Strong applied research instincts and quantitative rigor. You can formulate hypotheses, design meaningful experiments, establish credible baselines, and follow the evidence.
- Experience taking sophisticated ML or AI systems from research prototypes to real-world deployment.
- Demonstrated technical leadership across teams, including setting research and engineering direction, mentoring senior technical talent, and influencing major decisions through technical depth and evidence.
- Research or engineering experience with autonomous or multi-agent systems, long-horizon reasoning, adversary simulation, or closed-loop security systems.
- Experience with adversarial learning, reinforcement learning, model post-training, synthetic data, or other methods for improving AI systems through interaction and feedback.
- Experience building AI systems for high-volume, latency-sensitive cybersecurity environments.
- Experience architecting and building extensible AI or agentic platforms that enable teams to develop, evaluate, optimize, and deploy sophisticated AI applications at scale.
- Contributions to influential research, open-source software, or technical communities spanning AI and cybersecurity.
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Benefits of Working at CrowdStrike:
- Market leader in compensation and equity awards
- Comprehensive physical and mental wellness programs
- Competitive vacation and holidays for recharge
- Paid parental and adoption leaves
- Professional development opportunities for all employees regardless of level or role
- Employee Networks, geographic neighborhood groups, and volunteer opportunities to build connections
- Vibrant office culture with world class amenities
- Great Place to Work Certified™ across the globe
CrowdStrike is proud to be an equal opportunity employer. We are committed to fostering a culture of belonging where everyone is valued for who they are and empowered to succeed. We support veterans and individuals with disabilities through our affirmative action program.
CrowdStrike is committed to providing equal employment opportunity for all employees and applicants for employment. The Company does not discriminate in employment opportunities or practices on the basis of race, color, creed, ethnicity, religion, sex (including pregnancy or pregnancy-related medical conditions), sexual orientation, gender identity, marital or family status, veteran status, age, national origin, ancestry, physical disability (including HIV and AIDS), mental disability, medical condition, genetic information, membership or activity in a local human rights commission, status with regard to public assistance, or any other characteristic protected by law. We base all employment decisions-including recruitment, selection, training, compensation, benefits, discipline, promotions, transfers, lay-offs, return from lay-off, terminations and social/recreational programs-on valid job requirements.
If you need assistance accessing or reviewing the information on this website or need help submitting an application for employment or requesting an accommodation, please contact us at [email protected] for further assistance.
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Right to Work
CrowdStrike, Inc. is committed to fair and equitable compensation practices. Placement within the pay range is dependent on a variety of factors including, but not limited to, relevant work experience, skills, certifications, job level, supervisory status, and location. The base salary range for this position for all U.S. candidates is $235,000 - $350,000 per year, with eligibility for bonuses, equity grants and a comprehensive benefits package that includes health insurance, 401k and paid time off. For detailed information about the U.S. benefits package, please click here.
Expected Close Date of Job Posting is:12-16-2026
Salary Context
This $235K-$350K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2094 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 10,827 AI roles we're tracking, AI/ML Engineer positions make up 77% of the market. At CrowdStrike, 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 $212,160 based on 13,809 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($292K) sits 38% above the category median. Disclosed range: $235K to $350K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
CrowdStrike AI Hiring
CrowdStrike has 7 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer, AI Architect, MLOps Engineer. Positions span Remote, US, New York, NY. Compensation range: $250K - $375K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,431 positions. About 6% of all AI roles offer remote work.
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 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). 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 (122) are outnumbered by mid-level (5,256) and senior (3,922) 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 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 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 $213,480. Top-quartile roles start at $264,111, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 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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