Automate model testing and validation
Implement and operate CI/CD pipelines to enable safe, repeatable deployments and rollbacks
Provision and manage backend resources for inference (compute, containers, scaling), and tune performance
Define and continuously monitor health and performance metrics for deployed services
Own end-to-end REST API integration, connecting backend model services to product and platform surfaces
Work with researchers, evaluation engineers, product managers, and partner engineering teams to deliver production-ready solutions.

BS or MS in Computer Science, Computer Engineering, or equivalent industry experience
3+ years of professional software engineering experience building and operating production services
Experience automating testing and deployments using CI/CD, including release workflows that support safe rollouts and rollbacks
Experience building and operating cloud hosted, containerized services (for example Docker and Kubernetes or similar), including provisioning resources and scaling inference workloads
Experience building REST APIs using Python based frameworks (or similar), and integrating backend services with product or platform consumers
Strong software engineering fundamentals: version control, code quality, and writing maintainable, testable software
Strong written communication skills to document architectures, runbooks, and operational processes.

Senior Machine Learning Engineer, MLOps

Key skills