Machine Learning Engineering Technical Leader (hybrid) - 2009800

The application window is expected to close on: 05/01/2026 Job posting may be removed earlier if the position is filled or if a sufficient number of applications are received . Machine Learning Engineering Technical Leader (hybrid) - 2009800 *position requires a few days (varies) each week on site in San Jose office* Meet the Team: Leading Product and Test Engineering for Cisco Silicon One portfolio Who You Are: Designs, develops, and refines algorithms, including neural network architectures tailored for natural language processing and/or machine perception tasks, to create functional predictive models for diverse applications.  Utilizes a diverse array of training-time and inference-time techniques, including convolutional and transformer-based models, student-teacher models, distillation, and generative-adversarial networks (GANs), as well as the design of cost models and discriminators, to optimize model performance in terms of accuracy, efficiency, scalability, and reliability for real-world deployment. What You’ll Do: • Leverages a range of Cisco data and external data to identify opportunities and define a variety of hypotheses • Articulates the solution, defines its feasibility, understands its marketing requirements and communicates to program managers • Keeps abreast of industry changes and latest developments to aide in model and architecture selection and development • Guides teams in building and evolving model design practices for high-priority/ business critical ML models across multiple products/teams • Leads exploration of state of the art, emerging model architectures and algorithms that contribute to building Cisco's products • Leads automation of various steps in machine language workflow including model selection, training and inference • Designs novel and complex custom models • Architects' data splitting pipelines for specialized scenarios, creates optimization techniques for highly specialized model training efficiency and performance • Sets the technical vision and strategy for the Function or Portfolio's ML initiatives, including long-term model lifecycle management and continuous improvement • Leads research on and champions best practices for model integration and deployment using CI/CD principles Minimum Qualifications: Bachelors degree 12 years of related experience, or Masters degree 8 years of related experience, or PhD 5 years of related experience Developed, trained, and deployed machine learning models for real-world applications Trained and optimized machine learning models to improve quality yield and reduce test time across the Silicon One product portfolio Designed and implemented end-to-end ML pipelines (data ingestion, feature engineering, model training, evaluation, deployment, monitoring) Analyzed large, complex datasets to extract actionable insights and inform decision-making Collaborated with cross-functional teams including product, engineering, and business stakeholders Built scalable data processing systems using distributed frameworks  Optimize model performance, latency, and resource utilization in production environments Implemented A/B testing and experimentation frameworks to validate model impact Maintained and improved data quality, feature stores, and model monitoring systems Documented methodologies, models, and system architectures Preferred Qualifications Experience with deep learning, NLP, or computer vision Familiarity with MLOps tools (e.g., MLflow, Kubeflow, Airflow) Experience with big data technologies (e.g., Spark, Hadoop) Knowledge of containerization and orchestration (Docker, Kubernetes) Experience with real-time inference systems Strong understanding of software engineering best practices (CI/CD, version control, testing) SiOps 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 p ... (truncated, view full listing at source)