Staff Machine Learning Operations Engineer

Join Garner's Platform Engineering team as a Staff Machine Learning Operations Engineer. In this role, you will be responsible for the reliability, performance, and cost-efficiency of our production machine learning systems. You will lead the development of a robust platform for secure and consistent model deployment, ensuring the highest standards of production quality. Key responsibilities include owning the performance and functionality of ML systems, architecting the ML platform, implementing ML-specific CI/CD pipelines, and establishing drift monitoring. This position offers flexible PTO, hybrid and remote work options, and various financial benefits.

• Assumer la responsabilité de la fiabilité, de la performance et de l'efficacité des systèmes de machine learning en production.
• Diriger le développement d'une plateforme robuste pour le déploiement sécurisé et cohérent des modèles.
• Mettre en œuvre des pipelines CI/CD spécifiques au ML et établir des systèmes de surveillance de la dérive des données.
• Experience designing ML platforms or significant components of one (not strictly consuming SaaS) and the judgment to know when to build vs. buy
• 7+ years of software engineering experience, with significant time spent operating ML or data-intensive systems in production at scale
• Deep experience with the modern ML production stack: model serving (e.g., Sagemaker, Triton, or equivalent), feature stores, model registries, and CI/CD for ML
• Healthcare, regulated-data, or other high-stakes production ML experience is a plus but not required
• Strong collaboration with ML, data, platform engineers, data scientists, and product engineering teams, with the ability to set technical direction as the most senior MLOps voice in the org
• Strong infrastructure and platform engineering fundamentals: Kubernetes, containerization, cloud (AWS preferred), Terraform/IaC, observability, and incident response
• A desire to be a part of a high-performing, mission-driven team that operates with intense urgency, a strong sense of individual accountability, and a commitment to authentic feedback