About the role
Cloudinary empowers companies to deliver exceptional digital experiences by managing the entire media lifecycle at scale. Within Cloudinary’s R&D, the Research Group leads the development of cutting-edge algorithms for media understanding, generation, and optimization. We are seeking an experienced Staff Backend Engineer to lead the engineering efforts behind our homegrown platform for serving and operating production-grade AI models and AI based algorithms. This is a mission-critical role for someone passionate about building highly-scalable, GPU-aware, cloud-native systems that act as the connective tissue between algorithm research and product innovation. You will play a pivotal part in re-designing and evolving the platform, while supporting both research and application teams across the organization, and contributing to MLOps initiatives. \n Key
Responsibilities Platform Ownership Own the architecture, stability, scalability, and performance of the system. Design and implement platform features that support both synchronous low-latency and asynchronous compute-heavy algorithm execution. Enhance GPU management, scheduling, and resource allocation for optimal performance and cost-efficiency. Ensure robust Kubernetes-based deployment and observability for a highly dynamic system. Cross-Team Collaboration Act as the technical bridge between Research and Application teams by translating
requirements into scalable system designs. Collaborate closely with algorithm developers to streamline model deployment processes. Partner with backend engineers (primarily working in Ruby and Go) to integrate the research group algorithms into Cloudinary services. Engineering Excellence Advocate for high standards in code quality, observability, testing, and security. Guide engineering integration efforts when consuming the different platform APIs. Provide mentorship, support, and best practices to other engineers interacting with the platform. Take part in general R&D efforts, supporting a broader production environment. Platform Extension and MLOps Contribute to the evolution of our platform to support a wider range of algorithmic workloads and model types. Help shape tooling and infrastructure for model versioning, rollout, monitoring, and testing. Collaborate with DevOps and Infrastructure teams to maintain operational excellence, system observability, and robust infrastructure support Your