Staff Machine Learning Operations Engineer

Join Garner, a fast-growing company dedicated to transforming healthcare. As a Staff Machine Learning Operations Engineer, 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, directly influencing health outcomes and cost-effectiveness for millions of patients. This role offers real ownership and autonomy, along with the opportunity to work at the intersection of AI and systemic healthcare reform.

• 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 réduire les coûts et la latence grâce à une architecture améliorée.

• 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
• Experience designing ML platforms or significant components of one (not strictly consuming SaaS) and the judgment to know when to build vs. buy
• 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