Senior Machine Learning Engineer

- Design and implement machine learning solutions for operational and reliability use cases such as alert noise reduction and anomaly detection - Convert product requirements and reliability goals into clear machine learning challenges with well-defined metrics, such as false positive and negative rates, and impact on incident response times - Select and apply appropriate model families for various use cases, including supervised, unsupervised classical models, deep learning models, and language-based approaches when suitable - Collaborate with data engineering to establish and improve data pipelines for monitoring alerts, logs, metrics, and incident data - Develop features that capture temporal patterns and the relationships between services and infrastructure - Enforce data validation rules and quality checks while working on detecting and managing data drift and schema evolution - Set up and maintain a modern MLOps workflow, including experiment tracking, model registry, and automated deployment - Construct production-ready inference services that integrate with back-end systems and user interfaces - Work with DevOps on secure deployment strategies, including controlled rollouts and rollback plans - Define strategies for model retraining based on changing alert distributions and operational patterns - Create evaluation suites using historical incident data and realistic scenarios to monitor model performance - Develop dashboards to enhance visibility into model behavior and influence product decisions - Oversee monitoring of model performance and address degradation issues proactively - Incorporate feedback from users and experts for continuous improvement, including in active learning workflows - Adhere to security and compliance standards in regulated deployments, including access control and change management - Document model inputs, outputs, assumptions, and controls for review by relevant teams - Foster collaboration across teams to share machine learning components and patterns - Engage in architectural discussions to encourage reuse of patterns and components within the AI and data ecosystem - Mentor junior engineers and data scientists when necessary - Engage in on-site collaboration with local teams, contributing to a strong engineering culture through knowledge sharing and teamwork