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. 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 also a mission-driven company. We cultivate a culture that gives every CrowdStriker both the flexibility and autonomy to own their careers. 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 the industry-defining cybersecurity company. We are on a trajectory to $10B in revenue and beyond, and we are betting on Enterprise AI to supercharge that journey.
We are seeking a visionary Vice President to architect and lead our AI Center of Excellence (CoE). This is not a theoretical role; it is an operational leadership position tasked with deploying the next generation of agentic systems and AI-driven workflows across our entire enterprise. You will bridge the gap between technical possibility and business reality, translating complex AI capabilities into scalable solutions that drive GTM velocity, R&D innovation, and operational efficiency.
You will be the internal champion for our "AI-First" ambition, ensuring that every CrowdStriker—from HR to Sales to Engineering—is empowered by secure, resilient, and responsible AI.
What You'll Do:
Strategic Leadership & Governance
- Define the Enterprise AI Roadmap: Own the company-wide internal strategy for AI platforms, tools, and capabilities. Align AI initiatives directly with the $10B revenue goal and OPEX optimization targets.
- Lead the AI Center of Excellence: Build and manage a high-performing cross-functional team (Product Managers, Engineers, Analysts) to serve as the engine of AI transformation.
- Executive Leadership: Chair the AI SteerCo, driving prioritization, investment strategy, and buy-vs-build decisions with the C-Suite and executive leadership.
- Governance & Ethics: Develop rigorous frameworks for AI governance, compliance, and risk management. Ensure all internal AI adoption aligns with CrowdStrike’s standards for security and responsibility.
Execution & Transformation
- Transform through the coordinated deployment of agentic systems: Enable implementation of intelligent, autonomous workflows that redefine operational models across the enterprise.
- Cross-Enterprise Integration: Partner with functional business teams to embed AI into their daily operations, ensuring seamless integration into existing enterprise applications.
- Drive Productivity Step-Change: Identify and align teams to execute on opportunities to bend the OPEX growth curve with a focus on hyper-automation and process re-engineering.
- Innovation Scouting: Continuously assess the emerging technology landscape (Generative AI, LLMs, Autonomous Agents) to keep CrowdStrike at the forefront of internal innovation.
The Candidate Profile
We are looking for a rare hybrid: a strategist with the soul of a technologist, and a technologist with the polish of a management consultant.
What You'll Need:
- Executive Gravity: 15+ years of experience, with a proven track record of leading enterprise-wide transformation initiatives in high-growth, large-scale organizations.
- AI Fluency: Deep understanding of the current AI landscape, specifically Generative AI, agentic capabilities, and Large Language Models (LLMs). You know the difference between hype and high-impact utility.
- Strong Executive Leadership: Demonstrated ability to lead executive-level steering committees, influence C-level decision-making, and secure investment for technology initiatives.
- Technical Leadership: Experience managing technical teams (Data Science, Engineering) and partnering with Product Engineering organizations.
- Operational Rigor: Strong background in business process automation. You understand the ecosystems of CRM, ERP, and Enterprise Data Lakes and how to modernize them.
Preferred Attributes:
- Consulting DNA: Background in top-tier management consulting, with expertise in operational efficiency and digital strategy.
- Governance Expertise: Experience developing AI ethics policies, risk assessments, and regulatory compliance frameworks.
- "Customer Zero" Mindset: Experience implementing a company’s own technology internally to drive product feedback loops.
Why CrowdStrike?
CrowdStrike is the leader in cloud-delivered next-generation endpoint protection. We don’t just stop breaches; we define the future of security. By joining the IT leadership team, you will sit at the intersection of our business growth and our technological evolution. You will have the resources, the mandate, and the platform to build an AI legacy that the rest of the industry will study for years to come.
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CrowdStrike, Inc. is committed to equal pay for equal work in its compensation practices. The base salary range for this position in the U.S. is $300,000.00 - $340,000.00 per year + variable/incentive compensation + equity + benefits. A candidate's salary is determined by various factors including, but not limited to, relevant work experience, skills, certifications, job level, supervisory status, and location
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
recruiting@crowdstrike.com
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Expected Close Date of Job Posting is:03-27-2026
Salary Context
This $300K-$340K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $170K across 217 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $154,000 based on 8,743 positions with disclosed compensation. This role's midpoint ($320K) sits 108% above the category median. Disclosed range: $300K to $340K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
CrowdStrike AI Hiring
CrowdStrike has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $340K - $340K.
Remote Work Context
Remote AI roles pay a median of $160,000 across 1,226 positions. About 7% 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $145,600. 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 (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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