Sr. Machine Learning Engineer

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 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:

As an engineer in this team, you will play an integral role as we build out our ML Platform & GenAI Studio from the ground up. Since the launch of ChatGPT, # Phishing attacks has increased by 138% and hence the ML platform is a critical capability for Crowdstrike in its fight against bad actors. For this mission we are building a team in Bangalore. You will collaborate closely with Data Platform Software Engineers, Data Scientists & Threat Analysts to design, implement, and maintain scalable ML pipelines that will be used for Data Preparation, Cataloguing, Feature Engineering, Model Training, and Model Serving that influence critical business decisions. You’ll be a key contributor in a production-focused culture that bridges the gap between model development and operational success. Future plans include generative AI investments for use cases such as modelling attack paths for IT assets.

Location: Bangalore

Candidates must be comfortable to visit office once a week

What You’ll Do:

• Help design, build and facilitate adoption of a modern ML platform including support for use cases like GenAI
• Understand current ML workflows, anticipate future needs and identify common patterns and exploit opportunities to templatize into repeatable components for model development, deployment, and monitoring
• Build a platform that scales to thousands of users and offers self-service capability to build ML experimentation, training and inference pipelines
• Leverage workflow orchestration tools to deploy efficient and scalable execution of complex data and ML pipelines
• Champion software development best practices around building distributed systems
• Leverage cloud services like Kubernetes, blob storage, and queues in our cloud first environment

What You’ll Need:

• B.S. /MS in Computer Science or a related field and 10+ years related experience; or M.S. with 8+ years of experience;
• 3+ years experience developing and deploying machine learning solutions to production. Familiarity with typical machine learning workflows from an engineering perspective (how they are built and used, not necessarily the theory); familiarity with supervised / unsupervised approaches: how, why, and when and labelled data is created and used
• 3+ years experience with ML Platform tools like Jupyter Notebooks, NVidia Workbench, MLFlow, Ray etc.
• Experience building data platform product(s) or features with (one of) Apache Spark, Flink or comparable tools
• Proficiency in distributed computing and orchestration technologies (Kubernetes, Airflow, etc.)
• Production experience with infrastructure-as-code tools such as Terraform, FluxCD
• Expert level experience with Python; Java/Scala exposure is recommended. Ability to write Python interfaces to provide standardized and simplified interfaces for data scientists to utilize internal Crowdstrike tools
• Expert level experience with containerization frameworks
• Strong analytical and problem solving skills, capable of working in a dynamic environment
• Exceptional interpersonal and communication skills. Work with stakeholders across multiple teams and synthesize their needs into software interfaces and processes.

Bonus Points:

Critical Skills Needed for Role:

• Distributed Systems Knowledge
• Data/ML Platform Experience

Experience with the Following is Desirable:

• Go
• Iceberg (highly desirable)
• Pinot or other time-series/OLAP-style database
• Jenkins
• Parquet
• Protocol Buffers/GRPC