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's Data Science Studio is seeking a pioneering Senior MLOps Engineer to establish and lead our MLOps function from the ground up. As the first MLOps engineer in the studio, you will play a foundational role in shaping how we build, deploy, and scale machine learning systems that protect thousands of organizations worldwide.

This is a unique opportunity to define the technical strategy, influence the technology stack, and architect the infrastructure that will power our AI/ML-driven security solutions for years to come.

This role combines strategic vision with hands-on execution. You'll work at the intersection of data science, engineering, and production operations - building production-grade systems that operate at immense scale while collaborating closely with highly technical data scientists and ML engineering teams across CrowdStrike.

What You'll Do:

• Architect MLOps infrastructure from the ground up: Design and implement the foundational MLOps platform, establishing best practices, tooling, and workflows that will scale with our growing data science initiatives
• Define technology strategy: Evaluate, select, and integrate MLOps technologies and platforms that best serve our needs - from experiment tracking and model versioning to deployment pipelines and monitoring systems
• Build production-grade ML pipelines: Develop robust, scalable pipelines for model training, validation, deployment, and monitoring that handle massive data volumes and ensure reliability in production
• Enable data scientist productivity: Create tools, frameworks, and automation that empower data scientists to move quickly from research to production while maintaining high quality and reliability standards
• Establish monitoring and observability: Implement comprehensive monitoring, logging, and alerting systems to ensure ML models perform optimally in production and issues are detected proactively
• Drive MLOps culture and practices: Champion best practices in ML engineering, CI/CD for ML, model governance, and reproducibility across the data science organization
• Collaborate cross-functionally: Partner closely with data scientists to understand their workflows and pain points, and work with ML engineering teams to ensure seamless integration with broader platform capabilities
• Scale for the future: Design systems with scalability, security, and maintainability in mind, anticipating the needs of a rapidly growing ML portfolio
  
  

What You'll Need:

• 6+ years of experience in MLOps, ML engineering, DevOps, or related infrastructure roles with focus on machine learning systems
• Production ML systems expertise: Proven track record of building and operating ML systems at scale in production environments
• Strong infrastructure and automation skills: Deep knowledge of cloud platforms (AWS, Azure, or GCP), containerization (Docker, Kubernetes), and infrastructure-as-code (Terraform, CloudFormation)
• ML pipeline proficiency: Hands-on experience with ML workflow orchestration tools (e.g., Airflow, Kubeflow, MLflow, Metaflow) and building end-to-end ML pipelines
• Programming excellence: Strong coding skills in Python; experience with additional languages is a plus
• CI/CD and DevOps practices: Expertise in building automated deployment pipelines, version control, and modern DevOps methodologies
• Strategic and hands-on balance: Ability to think architecturally about long-term solutions while rolling up your sleeves to implement them
• Collaborative mindset: Excellent communication skills and ability to work effectively with data scientists, engineers, and stakeholders with varying technical backgrounds
• Startup mentality: Comfort with ambiguity and ability to build from scratch in a fast-paced environment
  
  

Why CrowdStrike?

Join at the ground floor of our MLOps journey and leave your mark on how we build AI/ML systems at scale. You'll have the autonomy to make critical technical decisions, work with exceptional talent, and build infrastructure that directly impacts the security of thousands of organizations worldwide. This is a rare opportunity to shape the future of ML operations in a leading cybersecurity company.

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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What You'll Need:

• 6+ years of experience in MLOps, ML engineering, DevOps, or related infrastructure roles with focus on machine learning systems
• Production ML systems expertise: Proven track record of building and operating ML systems at scale in production environments
• Strong infrastructure and automation skills: Deep knowledge of cloud platforms (AWS, Azure, or GCP), containerization (Docker, Kubernetes), and infrastructure-as-code (Terraform, CloudFormation)
• ML pipeline proficiency: Hands-on experience with ML workflow orchestration tools (e.g., Airflow, Kubeflow, MLflow, Metaflow) and building end-to-end ML pipelines
• Programming excellence: Strong coding skills in Python; experience with additional languages is a plus
• CI/CD and DevOps practices: Expertise in building automated deployment pipelines, version control, and modern DevOps methodologies
• Strategic and hands-on balance: Ability to think architecturally about long-term solutions while rolling up your sleeves to implement them
• Collaborative mindset: Excellent communication skills and ability to work effectively with data scientists, engineers, and stakeholders with varying technical backgrounds
• Startup mentality: Comfort with ambiguity and ability to build from scratch in a fast-paced environment
  
  

Why CrowdStrike?

Join at the ground floor of our MLOps journey and leave your mark on how we build AI/ML systems at scale. You'll have the autonomy to make critical technical decisions, work with exceptional talent, and build infrastructure that directly impacts the security of thousands of organizations worldwide. This is a rare opportunity to shape the future of ML operations in a leading cybersecurity company.