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. 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 proud to work for a mission-driven company leveraging AI to transform the way we work. CrowdStrikers drive their careers through flexibility and autonomy while also being expected to contribute to a culture of responsible AI adoption, experimentation, and innovation. We use an AI-first mindset as a force multiplier to proactively and continuously accelerate execution, build expertise, uncover insights, and solve complex problems. 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 is looking for a Sr. Machine Learning Engineer to join our growing AIDR Engineering Team. You will provide engineering support and solutions to a data science group focused on high-throughput, low-latency LLM inferencing. In conjunction with the other engineers and data scientists on the team, you will support LLM post-training, data engineering aligned to rigorous evaluations, and broadly supporting a mission that requires close engineering collaboration with applied data scientists. Substantively, the team has a big mission: allowing Enterprise users to interact safely and securely with AI applications. This is a front-seat role in designing for the AI age. CrowdStrike is a computer security company, but we do not require candidates for this role to have prior security industry experience. We will mentor and train in security topics as needed. We do expect a strong interest in CrowdStrike's mission and a willingness to engage with the needs of our product teams and customers.

What You'll Do:

• Innovate with the state of the art machine learning technology to accelerate data science.

• Focus on pragmatic support for fast-moving research and development teams.

• Implement high-quality solutions in support of customer-facing applications at scale.

• Providing in-depth analysis to identify potential vulnerabilities or gaps.

• Construct and maintain data pipelines,and contribute to the training and implementation of custom models.

• Collaborate across various teams to brainstorm, define, and devise solutions.

• Commit to ongoing learning and self-improvement.

• Stay attuned to our customers' challenges, always seeking ways to enhance support.

• Emphasize top-tier coding quality by adhering to best practices, rigorous testing, and thorough logging and metrics.

• Work within a collaborative and agile team environment.

• Contribute to mentoring fellow engineers across a spectrum of technologies and also absorb knowledge from them.

• Constantly explore ways to refine product architecture, knowledge models, user experience, performance, and reliability.

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

• Thrive in an environment that highly values trust.

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

• High level coding language such as JVM technologies or Python

• Docker

• Kubernetes

• AWS, GCP, or MaaS

• Kafka, Cassandra, and Spark

• ElasticSearch

• Terraform, Chef, or Ansible

• Experience with scaling inference across GPUs or GPU-clusters

What You'll Need:

• Prior work experience doing data engineering and architecture in support of advanced data science use cases.

• A deep understanding of LLM post-training methods and computational architectures.

• Understanding scalability and distributed systems e.g. sharding, partitioning, and concurrency.