CrowdStrike

This role focuses on building and facilitating adoption of a modern Data+ML platform, modularizing ML code, establishing repeatable patterns for model development, deployment, and monitoring, and building a scalable platform for ML experimentation pipelines. It involves leveraging workflow orchestration tools, cloud services, and CI/CD frameworks, with a future focus on generative AI use cases. The role emphasizes production-focused ML engineering and bridging the gap between model development and operational success.

This role focuses on building and scaling an ML Experimentation Platform, encompassing data preparation, feature engineering, model training, and model serving. It involves developing scalable ML pipelines, modularizing ML code, establishing patterns for model development and deployment, and leveraging workflow orchestration tools. The role also involves integrating with cloud services and CI/CD frameworks, with future plans for generative AI use cases.

CrowdStrike is seeking a Principal Software Engineer to lead the development of a next-generation endpoint AI learning framework. This role involves architecting and delivering a high-performance model delivery framework that leverages deep learning and machine learning on millions of endpoints worldwide, focusing on maintaining a lightweight footprint and optimizing resource utilization with hardware acceleration.

Software engineer role focused on building and maintaining distributed cloud services for advanced research in cybersecurity, with a specific mention of working with LLMs.

CrowdStrike is seeking a Senior Cloud Engineer to join their Sensor Foundations team, focusing on building and scaling cloud-based systems for cybersecurity threat detection. The role involves processing large volumes of data, integrating with machine learning models, and developing high-quality, reliable systems primarily in Go. Experience with distributed systems, cloud services, and stream processing is required.

Senior ML Platform Engineer focused on reliability and debugging of distributed ML infrastructure (Ray, Spark, Airflow, MLflow, Kubeflow, SLURM) for training and inference pipelines, optimizing performance, and building observability solutions.