Principal Engineer - Security ML Developer

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Job Description:

Why will you enjoy this new opportunity?

Today's enterprise networks face an expanding attack surface and an ever-changing threat landscape. The Anti-Malware Malware Group, part of the VMware by Broadcom - Advanced Network & Security Division (ANSD), is on a mission to meet this challenge by delivering powerful, cutting-edge malware detection capabilities. 

Our goal is to empower Security Analysts and Network Admins to stay ahead of the curve and to keep their environments safe and protected from advanced, targeted, and unknown malware. We do so by using a unique combination of advanced Sandboxing capabilities, and static and dynamic analysis of files and URLs to deliver a comprehensive and actionable report.

Join us on this epic adventure, and help build the next generation of malware detection tools leveraging the latest detection techniques and machine learning technologies, and most importantly, grow with us! 

Our team is responsible for developing, maintaining, and testing the malware detection capabilities for the VMware vDefend Advanced Threat Prevention (ATP), a cutting-edge networking and security analytics solution for detecting and preventing advanced threats.

This is an opportunity for you to collaborate closely with a group of talented engineers and security experts in an established team. 

Success in the Role: What are the performance outcomes over the first 6-12 months you will work toward completing?

• Architect and implement robust malware detection pipelines and scalable API services using advanced Python and SQL, ensuring high performance and reliability in production environments.

• Develop and optimize advanced malware detection algorithms leveraging your expertise in C/C++ and a deep understanding of PE/ELF file formats, enabling efficient identification of complex threats at the file level.

• Create and enhance static analysis tools for malware detection by applying in-depth knowledge of Assembly language, and developing techniques for identifying obfuscated or polymorphic threats.

• Lead the productization and deployment of detection components within Kubernetes (K8s) environments, using Docker and Helm for seamless integration, automation, and scaling of cybersecurity tools.

• Utilize your in-depth understanding of networking protocols, including VPN inner workings, TCP/UDP, and HTTP, to inform the development of network-based detection techniques and improve overall detection efficacy.

• Leverage cybersecurity expertise, including knowledge of exploitation techniques, YARA rule development, and machine learning classification models to innovate and refine advanced malware detection strategies across dynamic attack vectors.

• Design and implement high-performance, distributed data processing pipelines using frameworks such as Apache Beam, ensuring real-time data collection, analysis, and actionable insights for malware detection.

• Optimize and evaluate detection performance using Elasticsearch and Kibana, applying KQL queries to extract actionable intelligence, fine-tune detection rules, and drive continuous improvement of detection capabilities.

• Foster Collaboration and Knowledge Sharing: Establish strong working relationships with colleagues across engineering. Actively participate in knowledge-sharing initiatives to help others understand complex technical topics.

The Work: What type of work will you be doing? What assignments, requirements, or skills will you be performing on a regular basis?

Our team's work life is very dynamic. A weekly sync-up meeting helps us connect, share progress, ask for help, or determine how to best collaborate and tackle the challenges we face. With our game plan in place, we jump into execution and continue collaboration via asynchronous communication. Once every four weeks, we convene, discuss, and choose our commitments for the upcoming Sprint. Each team member self-manages and self-prioritizes work based on