Corelight

• Lead technical design discussions to build and operate elastic, secure microservices using AWS serverless architectures (Lambda, API Gateway). • Design and maintain robust Machine Learning and Data Analytics pipelines, utilizing Apache Kafka or similar technologies for real-time data ingestion and transformation. • Develop REST and GraphQL services designed to interface with massive datasets, providing the backend power for a rich, data-heavy visual user interface. • Work within an Agile team to take features from initial design through to production deployment, monitoring, and iterative improvement. • Evangelize and implement best practices for code health, testing stability, maintainability, and overall system quality. • Partner with product and architecture teams to influence product direction and translate complex security requirements into reliable software solutions.

• Contribute to curriculum development, keeping material current with real-world network-based attack patterns you've encountered operationally • Help build hands-on lab environments and CTF challenges that reflect realistic adversary behavior, not textbook scenarios • Co-facilitate and independently lead training sessions (virtual and in-person) for technically experienced audiences • Develop recorded, on-demand curriculum • Administer and optimize Learning Management Systems (Skilljar experience is a plus) • Educate students on the use and application of Corelight for threat hunting, incident response, and detection engineering • Use AWS and scripting to help maintain and improve lab infrastructure and provisioning workflows • Up to 50% travel expected

• Lead to the full range of development stages for AI/ML projects, from early explorations to productization and maintenance. • Work closely with data scientists and network security experts to design and implement scalable ML pipelines for large datasets. • Design and implement strategies to deploy LLMs and for fine-tuning. • Conduct experiments, analyze results and resource consumption using various metrics and visualization techniques. • Drive adoption of ML development best practices in code health, quality, test stability and maintainability. • Participate in technical discussions within the Labs team and collaborate with other teams across the organization. • Work in an Agile development team focused on exploring and delivering ML use cases.