Machine Learning Ops Engineer

This ML Ops Engineer role focuses on building and maintaining the infrastructure for machine learning model deployment and monitoring. The position involves automating ML pipelines, managing model lifecycle, and ensuring production reliability.

This senior MLOps role focuses on operationalizing machine learning models, building and maintaining CI/CD pipelines for ML, and ensuring model monitoring and reliability. The engineer will work with data scientists to deploy and scale ML models in production environments.

Diese Rolle konzentriert sich auf den Betrieb von Daten- und KI-Systemen, einschließlich Überwachung, Wartung und Optimierung. Erforderlich sind Erfahrung mit DevOps-Praktiken und Datenpipelines, um eine zuverlässige und effiziente Systemleistung sicherzustellen.

This role builds and maintains ML infrastructure for the Experiences team, focusing on deploying and monitoring machine learning models. It requires strong software engineering skills and experience with MLOps tools.

This role defines and enforces marketplace trust and safety standards, including partner vetting, content policies, and advertising model governance. The manager will write policies, monitor data for fraud trends, and partner with data science and enforcement teams to optimize workflows. Requires 5+ years of experience in trust and safety or marketplace operations, strong SQL skills, and knowledge of AI-generated content issues.

This newly created role owns the full brand lifecycle on impact.com's Marketplace, from onboarding and activation to engagement and re-engagement. The manager will design and execute lifecycle strategies, build scalable communications across in-app, email, and notifications, and leverage AI tools for automation. Requires 3+ years of experience in growth or lifecycle marketing, strong data analysis skills, and experience with A/B testing.

This Senior API Data Analyst role maintains a company-wide view of API usage, costs, and integration health, turning observability into actionable guardrails. The analyst automates checks across logs and data warehouses, identifies misuse and fan-out risks, and collaborates with engineering teams to set rate limits and improve integration patterns. Requires strong technical skills in data analysis and API ecosystems.