Über
We are seeking a Machine Learning Engineer to build dynamic troubleshooting agents that don't just monitor networks—they understand them. Our team is solving for the massive complexity of unstructured production log data, optimizing hardware utilization for data collection, and automating the creation of synthetic datasets that push the boundaries of what LLMs can achieve in network configuration and remediation. You won't just be maintaining pipelines, you will be architecting the data infrastructure that allows our models to reason through real-world network failures in real-time. Responsibilities: Design and scale automated pipelines that transform raw, high-velocity production logs into high-quality insights. Develop systems that generate synthetic data, enabling our models to learn from edge cases that rarely occur in the wild. Solve network complexity problems by tackling the unique challenges of time and network state dependencies to improve the accuracy of our agents. Optimize at scale through efficient data collection and hardware utilization, ensuring our ML infrastructure remains performant as our agent capabilities expand. Help shape the future of Network AI by collaborating directly with researchers to define the next generation of AI-driven network management for Cisco network products such as Meraki, ThousandEyes, Catalyst Center and Nexus. Minimum Qualifications: Bachelor's degree in STEM and 5+ years of relevant experience, or Master's degree in STEM and 3+ years of relevant experience and/or PhD in STEM +0 years of relevant experience or equivalent related work experience 5+ years of experience in data engineering, machine learning engineering, or related roles. Data Pipeline experience, designing and scaling data pipelines for unstructured or semi-structured data, including ingestion, cleansing, and auditing. ML Infrastructure experience working with ML data workflows, including dataset creation, labeling, and evaluation. Experience with Python and data processing frameworks (e.g., Spark, Beam, Ray). Experience with ML systems and tools, such as training pipelines and model evaluation frameworks. Preferred Qualifications: Experience with human-in-the-loop ML systems, active learning, weak supervision or self-evolving agents. Exposure large language models, computer vision, or speech datasets. Experience building internal tools or platforms used by annotation or operations teams. Why Cisco?
At Cisco, we're revolutionizing how data and infrastructure connect and protect organizations in the AI era – and beyond. We've been innovating fearlessly for 40 years to create solutions that power how humans and technology work together across the physical and digital worlds. These solutions provide customers with unparalleled security, visibility, and insights across the entire digital footprint. Fueled by the depth and breadth of our technology, we experiment and create meaningful solutions. Add to that our worldwide network of doers and experts, and you'll see that the opportunities to grow and build are limitless. We work as a team, collaborating with empathy to make really big things happen on a global scale. Because our solutions are everywhere, our impact is everywhere. We are Cisco, and our power starts with you.
Sprachkenntnisse
- English
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