Cisco Swarm Machine Learning Engineer 

United States, Georgia, Atlanta 

527974004

The application window is expected to close on 8/19/2025.

Job posting may be removed earlier if the position is filled or if a sufficient number of applications are received.

• Lead by example as a hands-on architect and technical expert who designs and builds cutting-edge systems that detect and analyze security threats.
• Use deep understanding of the threat landscape to propose innovative solutions to counter threats targeting Cisco’s customers.
• Leverage modern AI/ML techniques to improve the accuracy of threat detection solutions and automate/accelerate manual analysis processes.
• Develop and implement advanced machine learning models across different hardware environments (including cloud and network edge); models may include adapting neural network architectures or creating novel ones to address challenges.
• Drive the training, validation, and fine-tuning of models, developing methods to identify performance metrics especially of the hardware accelerated models.
• Analyze and extract significant patterns in high-dimensional data spaces using advanced techniques.
• Implement robust software systems for integrating and maintaining machine learning models
• Collaborate with software engineering teams to design primary deployment strategies for machine learning models into security systems.
• Establish and maintain best practices for machine learning and security operations, including clear documentation of models and procedures.

• 7+ years of related security experience, specifically in the areas of network security or malware analysis
  
• Experience developing robust and scalable code for cybersecurity analytics
• Experience with state-of-the art machine learning techniques and libraries
• Experience with writing high-quality code and modern application development frameworks

• Bachelor’s degree or higher in Computer Science or related field
• A strategic problem solver in the areas of threat detection and analysis
• Ability to get consensus and set technical direction within a large engineering organization
• Experience optimizing machine learning or deep learning models for specific hardware
• Familiarity with hardware acceleration libraries (e.g., Morpheus, cuDNN, TensorRT, OpenVINO).
• Experience with containerization technologies (e.g., Docker, Kubernetes) in the context of hardware-specific deployments
• Knowledge of cybersecurity concepts and threat detection methodologies