Sr. Machine Learning Engineer (Remote)

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 team is expanding – we are looking for a Senior Machine Learning Engineer to join our growing Data Science organization. You will build scalable and resilient systems which help train, evaluate, and integrate some of the largest and most complex machine learning models in the industry. You will also have the opportunity to apply your expertise in software engineering and knowledge of ML to accelerate scientific research in areas such as: algorithm optimization, algorithm scaling, and modeling automation.

The scale of our systems and data are approaching Exabytes in size. Experience with extremely large-scale systems, including DevOps patterns, practices, and standards are important for this work.

What You’ll Do:

• Apply deep knowledge of distributed systems to improving existing and new model training systems and scale novel data processing techniques

• Apply machine learning knowledge to quickly understand and build automated systems that encapsulate manual ML research workflows

• Champion proper engineering and architectural approaches to ML workflows with scientists, engineers, analysts, architects and management

• Productionize research projects by optimizing code and systems to perform within various engineering performance envelopes

• Employ and evolve established CrowdStrike tools and services to build solutions for detecting and countering targeted cyber assaults.

• Apply software engineering best practices to machine learning code bases and systems to improve reliability and observability 

• Automate and visualize analyses, results and processes in our artificial intelligence and machine learning pipeline

• Own your work with autonomy, end to end: develop, test, deploy and monitor your changes

Tech Stack (not mandatory to know everything; a robust learning capacity is essential):

• Python (xgboost, pytorch, tensorflow, scikit-learn, pandas, huggingface)

• Docker

• Kubernetes

• AWS, GCP

• Spark, Ray

• MLFlow, Airflow

• Terraform, Jenkins

What You’ll Need:

• Proven track record working with distributed compute systems (e.g. Spark, Ray, etc) running on a cloud provider such as AWS or GCP

• Versed in standard machine learning techniques, algorithms, and how to tune them

• Previous experience working with extremely large datasets of high-dimensional data

• Thorough understanding of engineering best practices from appropriate testing paradigms to effective peer code reviews and resilient architecture.

• The skills to meet your commitments on time and produce high-quality software that is unit tested, code reviewed, and checked in regularly for continuous integration.

• Resourcefulness - ability to work independently with minimal supervision to solve complex problems

• Thirst for knowledge - do not hesitate to step outside of your comfort zone to learn new technologies, algorithms and concepts

Bonus Points:

• Prior experience in cyber security

• Experience with DevOps topics such as proxies, subnets, vpcs, identity and access management

• Understanding of compute scalability trade-offs for hyperparameters in standard machine learning models

• Experience with deploying and managing systems on Kubernetes

• Experience working with GPU-based training systems at