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. 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:
The security domain presents one of the richest and most consequential training signal environments in applied AI. It’s adversarial by nature, grounded in real operational outcomes, and evolving faster than any static benchmark can capture. We're building the post\-training and reinforcement learning capability to build the latest models and harnesses into security\-specialized systems that reason, plan, and act across complex cyber workflows. The person leading this work will be in the research, not just directing it.
In this role, you'll own the full post\-training stack for security\-domain AI (e.g., supervised fine\-tuning, reward modeling, RLHF and RLAIF pipelines, and agent\-RL environments) and the agentic research that sits on top of it. That means designing, building, and evaluating the harnesses that security agents actually run on (e.g., the scaffolding, tool\-use interfaces, planning loops, memory and context management, and multi\-step execution frameworks) that determine whether a trained model can operate reliably on complex security tasks. Post\-training and agent architecture are not separable problems in this work. The reward signal you design has to reflect what the harness can measure, and the harness has to be built to surface what training needs to optimize. You'll set the technical direction on both, and you'll be in the work on both.
You'll lead a team of research scientists and engineers, but the team will look to your own work as the standard. The successful candidate shapes research priorities, keeps the team moving at high velocity across multiple training cycles per year, and elevates the quality of work by staying close enough to it to know what good actually looks like.
What You'll Do:
- Own and personally drive the full post\-training pipeline for security\-domain AI — SFT, RLHF/RLAIF, agent\-RL, and reward modeling. Set research priorities and architectural direction, and lead experimental work on the hardest problems yourself rather than delegating them away. Design reward modeling methodology grounded in verified security outcomes rather than proxy signals, drawing on both human expert feedback and automated adversarial evaluation. Define data curation standards across sourcing, filtering, quality scoring, and domain weighting that drive measurable capability improvement.
- Build and maintain agent\-RL training environments that simulate realistic cyber workflows (multi\-step offensive and defensive tasks, tool use, and long\-horizon planning) contributing directly to environment design and reward shaping. Lead the design and build of the agent harnesses that run on top of those trained models: scaffolding architecture, tool\-calling interfaces, planning and reasoning loops, and memory and context management. Treat harness design with the same rigor as the training pipeline; these systems determine whether strong post\-training translates into reliable, trustworthy behavior in the field.
- Develop and own evaluation methodology for the full agentic stack, not model capability in isolation, but harness behavior, tool\-use reliability, planning coherence, and end\-to\-end task completion across realistic security workflows. Define the benchmarks, red\-line tests, and measurement practices that give the team and the organization genuine confidence that an agent works.
- Partner closely with other teams to ensure post\-training and agentic work integrates cleanly with the broader model development loop. Contribute original research through publications, external presentations, and open\-source artifacts where appropriate, building CrowdStrike's credibility as a research\-first organization in this space.
- Recruit, develop, and retain a high\-density team of research scientists and ML engineers. Set a technical bar through your own contributions, not just your standards.
What You'll Need:
- MS or PhD in computer science, machine learning, or a related quantitative discipline.
- 8\+ years of experience in ML research or engineering, with meaningful depth in large language model post\-training.
- Hands\-on expertise across the modern post\-training stack, including SFT data pipelines, RLHF/RLAIF, PPO or similar RL algorithms applied to language models, and reward model design and training. This means you've done the work, not managed people who have.
- Demonstrated experience designing or building agentic system harnesses for LLM\-based agents, including tool\-use frameworks, planning scaffolds, multi\-step execution environments, and context or memory management. You've built these systems, not just used them.
- Strong evaluation instincts: experience designing evaluation protocols that are resistant to overfitting, capable of measuring genuine capability improvement, and interpretable to both technical and non\-technical stakeholders.
- Track record of running high\-velocity research programs with disciplined tracking and fast iteration.
- Proven ability to lead and grow research teams while remaining a credible, active technical contributor.
Ways to Stand Out:
- Demonstrated experience building or operating RL training environments for language model agents, including environment design, rollout infrastructure, and reward shaping.
- Experience applying post\-training or RL techniques in security, adversarial ML, or other high\-stakes operational domains where ground truth is expensive and noisy.
- Deep hands\-on experience with agent harness architecture applied to long\-horizon, multi\-step task environments where reliability and failure modes matter as much as peak capability.
- Background designing synthetic data pipelines or simulation environments for agent training in complex, tool\-using workflows.
- Familiarity with the offensive or defensive security practitioner's workflow — penetration testing, detection engineering, incident response, or threat intelligence — sufficient to reason about what good model behavior looks like in practice.
- Published research in post\-training, RLHF, RL for language agents, or related areas at top\-tier venues (NeurIPS, ICML, ICLR, ACL, or equivalent).
- Experience working on and adapting open\-weight base models (Llama\-class, Qwen\-class, or similar) for domain\-specialized continued training and fine\-tuning.
\#LI\-JF1
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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.
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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 $195,000 \- $290,000 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:08\-11\-2026
Salary Context
This $195K-$290K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($242K) sits 34% above the category median. Disclosed range: $195K to $290K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
CrowdStrike AI Hiring
CrowdStrike has 7 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, Data Engineer. Positions span Remote, US, CA, US. Compensation range: $180K - $290K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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