Machine Learning Engineer
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Job Description
About Cisco ThousandEyes:
Cisco ThousandEyes is a Digital Experience Assurance platform that helps organizations ensure flawless digital experiences across all networks, including those they don’t directly manage. Leveraging AI and a comprehensive collection of cloud, internet, and enterprise network telemetry data, ThousandEyes allows IT teams to proactively identify, diagnose, and resolve issues before they impact end-users. It’s deeply integrated with the Cisco technology portfolio and other systems, enabling large-scale deployments and providing AI-driven insights within Cisco’s Networking, Security, Collaboration, and Observability offerings.
About the Job: Machine Learning Engineer (Alerts Team):
This role focuses on developing and optimizing anomaly detection algorithms for a highly scalable, real-time stream processing platform. The work combines handling massive datasets with innovative machine learning applications to deliver actionable insights to customers.
Key Responsibilities:
• Collaborate with a team to design, implement, and maintain large-scale AI/ML pipelines for real-time anomaly detection.
• Train and tune models, and conduct model evaluations using Deep Learning, Machine Learning (AI/ML) Models, and Large Language Models to detect anomalies across billions of events.
• Design and implement sophisticated anomaly detection algorithms (e.g., Isolation Forests, LSTM-based models, Variational Autoencoders) tailored to specific data streams.
• Create robust evaluation frameworks and metrics to assess algorithm performance.
• Implement and optimize stream processing solutions using technologies like Flink and Kafka.
• Work with diverse and large-scale data, pushing the boundaries of real-time anomaly detection.
Qualifications:
• 3-5 years of software development experience and a minimum of 2 internships with direct experience in building and evaluating ML models and delivering large-scale ML products.
• MS or PhD in a relevant field.
• Proficiency in crafting machine learning models, including neural networks (transformer models, Large Language Models), decision trees, and other traditional machine learning models. Ability to translate concepts into practical solutions.
• Fluency in machine learning frameworks such as SKLearn, XGBoost, PyTorch, or TensorFlow. Ability to leverage code for innovative solutions.
• Proficiency in Python and the ability to translate abstract machine learning concepts into robust, efficient, and scalable solutions.
• Strong Computer Science fundamentals and object-oriented design skills.
• Experience building large-scale data processing systems.
• Experience working in a fast-paced development environment.
• Strong team collaboration and communication skills.
