Interested in this AI/ML Engineer role at CrowdStrike?
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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:
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CrowdStrike is seeking a Software Developer in Test (SDET) to join our AI Detection and Response (AIDR) team. In this role, you will own end\-to\-end quality for the next generation of AIDR services — a platform that processes millions of AI security events per second and manages petabytes of critical AI data. This is a hands\-on, coding\-intensive role on par with product development. You'll build and own AIDR\-specific test tooling and frameworks, while leveraging and contributing to shared organization\-wide testing infrastructure. Your work will span validation across both cloud\-side backend services and UI applications, ensuring the system behaves correctly at every stage — from individual components to full system behavior. Testing concerns span functional, performance, contract, and chaos/resilience engineering.
This is an opportunity to work at unprecedented scale with cutting\-edge cloud technologies while making a direct impact on organizations worldwide, protecting them from sophisticated AI\-based threats.
PLEASE NOTE: This role is hybrid, requiring 2\-3 days per week on\-site at one of the posted locations.
Success Means:
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- Architect enterprise\-grade test solutions that validate systems scaling seamlessly from thousands to millions of requests per second
- Building reusable test frameworks and platform libraries that accelerate quality across multiple engineering teams
- Drive operational excellence through rapid issue resolution, thorough root cause analysis, and proactive system improvements
- Collaborating effectively across engineering teams to drive testing best practices and quality culture
- Deliver with velocity while maintaining the highest standards of security and reliability
What You'll Do:
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- Own end\-to\-end validation of AIDR services across cloud\-side backend and UI layers, spanning component, integration, and application levels, while continuously improving test and validation processes
- Build and own AIDR\-specific test tooling and frameworks, while leveraging and contributing to shared organization\-wide testing tools and infrastructure
- Define and drive testing strategies across functional, performance, contract, and chaos/resilience testing concerns
- Verify features and functionality spanning our processing, data, and customer\-facing application layers to ensure reliability, accuracy, and performance at scale
- Participate in code and design reviews with a focus on quality, testability, security, and code hygiene
- Review engineering technical design documents and requirements; provide guidance and strategy on how and where to build in testability
- Contribute to service operations, including improving overall service observability and monitoring, root cause analysis of production issues, and implementing quality improvements to prevent future outages
- Increase and improve automated test coverage across both backend services and UI applications
- Mentor team members on testing methodologies, secure coding practices, and quality engineering
- Leverage new open source solutions and AI\-powered tools to build innovative testing approaches
What You'll Need:
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- 8\+ years of experience as an SDET or Backend/UI Developer (combined)
- 3\+ years building testing frameworks and tooling for a Cloud SaaS product or similar SOA
- Experience contributing to or defining testing strategy at a team or organizational level
- Strong debugging skills: ability to spot design flaws, race conditions, and performance bottlenecks in complex distributed architectures
- Strong computer science fundamentals (algorithms, data structures, distributed systems)
- Expertise in at least one of: Golang, Python, Java — Go is our primary backend language, but strong engineers in any of these are welcome and we support ramp\-up
- Experience in a cloud distributed systems stack: e.g., Kubernetes, PostgreSQL, Redis, Kafka, and/or similar technologies — transferable skills in each category are a must
- Experience with cloud services such as AWS, GCP, or OCI, particularly compute and storage offerings
- Experience with CI/CD pipelines and integrating automated testing into deployment workflows
- Experience developing and deploying in containerized environments (e.g., Docker, Kubernetes)
- Extensive experience with testing tools and best practices (e.g., Jest, QUnit, Cypress, Playwright, JUnit, TestNG)
- Experience testing REST and/or gRPC APIs and real\-time or high\-volume data systems
- Proven experience utilizing AI technologies to enhance decision\-making, streamline workflows and processes, improve efficiency, and drive business outcomes
- Strong cross\-group collaboration and interpersonal communication skills working across engineering organizations
- Experience mentoring engineers and driving quality culture across a team or organization
Bonus Points:
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- Previous experience in cybersecurity companies or security\-focused products
- Deep understanding of AI/ML security challenges, threats, and mitigation strategies
- Experience building, operating, validating, and scaling low\-latency cloud services using gRPC and event\-driven messaging systems such as Kafka or Pulsar
- Experience with UI testing frameworks and front\-end validation (Ember.js, React, or similar)
- Experience with data visualization testing and accessibility validation
- Experience with performance measurement, profiling, and optimization (both backend and frontend)
- Experience with chaos engineering and resilience testing
- Hands\-on experience with Oracle Cloud Infrastructure (OCI), Google Cloud Platform (GCP), and/or Microsoft Azure
- Experience leveraging AI and agentic tools to enhance testing workflows and automation
- Contributions to open source libraries and/or frameworks
- Familiarity with MS CoPilot Ecosystem and MS Agent Studio
\#LI\-MF1
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 $120,000 \- $180,000 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.
Salary Context
This $120K-$180K range is below the median 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 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 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($150K) sits 30% below the category median. Disclosed range: $120K to $180K.
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.
CrowdStrike AI Hiring
CrowdStrike has 13 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, Research Scientist. Positions span Sunnyvale, CA, US, Remote, US. Compensation range: $180K - $350K.
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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