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:
This role is part of CrowdStrike’s Core Tech, Go To Market IT Apps Team — a high\-performing organization delivering scalable, secure, and intelligent GTM capabilities that accelerate business velocity and operational excellence. As part of the GTM AI Pod, team members are expected to embrace Agentic AI technologies, leverage modern AI engineering practices, and contribute toward building next\-generation intelligent GTM workflows.
As a GTM AI Deployment Strategist, you will serve as a hands\-on technical leader responsible for deploying, scaling, and overseeing delivery of enterprise AI solutions across CrowdStrike’s GTM ecosystem. This is not a traditional Product Manager or pure Development role. Instead, the role bridges business strategy, AI solution implementation, workflow orchestration, stakeholder alignment, and execution leadership.
You will collaborate closely with Product Managers, Developers, Architects, and Business Stakeholders to transform business requirements into scalable AI\-enabled solutions. The role requires strong stakeholder management skills, technical depth in AI technologies and integrations, and the ability to drive end\-to\-end delivery while remaining hands\-on in solution design, deployment planning, orchestration patterns, and implementation guidance.
What You’ll Do:
- Lead the design, deployment, and operationalization of agentic AI workflows across GTM platforms (Salesforce, Slack, Marketo, CPQ, Snowflake and enterprise systems).
- Drive end\-to\-end delivery of enterprise AI initiatives by partnering with Product Managers, Engineering teams, Security, and Business Stakeholders across the GTM ecosystem.
- Define scalable integration patterns for LLM\-powered agents, RAG pipelines, orchestration frameworks, and enterprise AI services using LangGraph, CrewAI, MCP, or similar technologies.
- Oversee implementation of intelligent workflows with human\-in\-the\-loop escalation, retry handling, observability, and governance controls.
- Establish AI deployment standards, reusable design patterns, and operational guardrails for the GTM AI Pod engineering teams.
- Collaborate closely with developers and solution teams while remaining hands\-on in solution design, deployment planning, technical reviews, and implementation guidance.
- Lead architecture and delivery reviews to ensure AI solutions are scalable, secure, maintainable, and aligned with enterprise standards.
- Define standards for Agentic Development Life cycle , operating model and deployment of agents on SAAS/On\-prem infrastructure
- Partner with Information Security teams to ensure responsible AI practices, secure data handling, and policy controlled compliant AI workflows.
- Drive stakeholder alignment across business, technology teams and leadership team for strategic AI programs.
- Evaluate and recommend AI platforms, vector databases, orchestration tooling, and enterprise integration strategies.
AI \& Open\-Source Mindset
- Champion adoption of Agentic AI and LLM\-powered tooling across GTM engineering and operational workflows.
- Promote automation\-first thinking and pragmatic application of AI/ML technologies to enterprise GTM systems.
- Stay current with rapidly evolving AI ecosystems, orchestration frameworks, and enterprise AI best practices.
- Encourage adoption of open standards and open\-source AI tooling wherever appropriate.
- Operate with responsible AI principles including security, transparency, governance, and data privacy by default.
- Contributions to open\-source AI ecosystems or AI engineering communities.
What You’ll Need:
- Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or related field.
- 10\+ years of experience in enterprise technology, solution delivery, distributed architecture, or product engineering environments.
- Experience leading enterprise Agentic AI transformation initiatives.
- 3\+ years of hands\-on experience designing and deploying AI/LLM\-powered enterprise solutions.
- Strong experience leading cross\-functional programs and managing stakeholder relationships across business and technology organizations.
- Deep understanding of AI orchestration frameworks such as LangGraph, CrewAI, LangChain, Semantic Kernel, or similar platforms.
- Experience with LLM APIs and enterprise AI platforms including OpenAI, Anthropic, AWS Bedrock, or equivalent ecosystems.
- Experience implementing RAG architectures, vector databases, AI observability, and intelligent workflow orchestration.
- Experience with AI governance, prompt security, and AI risk mitigation frameworks.
- Strong understanding of enterprise integrations, APIs, event\-driven architectures, and GTM platforms (Salesforce preferred).
- Experience guiding engineering teams through implementation while remaining hands\-on in solution design and deployment strategy.
- Strong communication, executive presentation, and technical leadership skills.
Bonus Points:
- Familiarity with MCP (Model Context Protocol) and enterprise AI interoperability patterns.
- Experience with GTM and revenue platforms such as Salesforce, Gong, Clari, Outreach, or Salesloft.
- Familiarity with enterprise AI observability and monitoring platforms such as LangSmith or OpenTelemetry.
\#LI\-NA1
\#LI\-Remote
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 $145,000 \- $220,000 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 $145K-$220K range is above the median 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. Mid-level AI roles across all categories have a median of $165,000. Disclosed range: $145K to $220K.
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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