Principal Staff Engineer, AI Platform Research, Data Science (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 hands-on Staff Data Engineer with deep expertise in Large Language Models (LLMs), Agents and AI platforms to join our growing Data Science Platform Engineering Team. You will be a key technical leader, responsible for designing, building, and deploying cutting-edge data infrastructure that powers our next generation of AI-driven security products.

Given the rapid evolution of AI tooling and development standards, we seek a technical leader to stay at the forefront of research, optimizing these systems while driving the platform advancements that accelerate our research teams. This role requires significant hands-on experience in LLM integration, agentic workflows, and agent harnessing to deliver high-impact, scalable solutions.

You will champion by hands on executing engineering excellence, focusing on shipping fast, writing elegant, high-quality code, and actively mentoring and strengthening the team's technical knowledge and capabilities.

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

What You'll Do:

• Write code everyday.  Architect, implement, and optimize data platforms and pipelines specifically designed to support LLMs, Neural Networks, Retrieval-Augmented Generation (RAG), and sophisticated AI agentic systems at Exabyte scale.
• Drive the adoption and deployment of agentic workflows and agent harnessing techniques to create autonomous, data-driven security features.
• Lead by example in hands coding highly scalable, fault-tolerant, and cost-effective data solutions, emphasizing rapid iteration and high-quality deployment.
• Write elegant, production-ready code with a focus on performance, maintainability, and testing rigor, ensuring the ability to ship fast without compromising quality.
• Provide technical leadership and deep expertise in data modeling, normalization, and semantic cataloging for AI/ML workloads.
• Establish best practices for MLOps/DataOps surrounding LLMs, including monitoring, observability, and zero-touch recovery mechanisms for AI services.
• Actively mentor engineers, conducting technical workshops, leading design reviews, and strengthening the team's knowledge in cutting-edge AI platform technologies.
• Collaborate across the organization with Data Scientists, Product Managers, and other engineering teams to transform research prototypes into robust, production-grade services.
• Own the end-to-end lifecycle of critical data services: development, testing, deployment, and monitoring.
• Tech Stack: Languages: Expert-level proficiency in at least one — Python, Go, Rust, or JVM technologies.
• AI/ML: MLOps tools (MLflow, SageMaker, Vertex AI); agentic frameworks (LangChain, LlamaIndex).
• Distributed Processing: Spark, Dask, or Flink.
• Cloud: AWS, GCP, or OCI and related data services.
• Containers & Orchestration: Docker, Kubernetes.
• Streaming: Kafka, Pulsar.
• Data Warehousing & Orchestration: Snowflake, BigQuery, Airflow, Kubeflow.

What You'll Need:

Bachelor’s, Master’s or PhD in Computer Science, Data Engineering, or a related STEM field, or equivalent practical experience.

5+ years of progressive experience in Data Engineering/Platform Engineering, with at least 3 years focused on architecting and building platforms for AI/ML or Data Science at massive scale.

Demonstrable hands-on experience in LLM engineering (fine-tuning, prompt engineering, deployment), RAG, and developing agentic workflows.

Proven track record of designing and delivering large-scale distributed systems (sharding, partitioning, concurrency).

Exceptional ability to write clean, elegant, performant, and well-tested code, coupled with a proactive mindset for delivering results quickly.

Ability to leverage AI aided secure code development

A thorough understanding of engineering practices, including effective peer code reviews, resilient architecture design, and comprehensive testing paradigms.

Prior experience in a  Staff level engineering role, demonstrating technical leadership and mentorship capabilities.

Proven experience utilizing AI technologies to enhance decision-making, streamline workflows and processes, improve efficiency and drive business outcomes.

Bonus Points:

Prior experience in the cybersecurity, intelligence, or high-compliance industries.

Contributions to open-source projects related to data or AI/ML.

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