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.

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 Qualifications

• 8+ years of experience in software engineering, with 3+ years working on infrastructure/platforms involving ML/AI, GPU, or data-heavy systems
• Proficiency in Python and familiarity with backend languages such as Ruby and/or Go
• Strong understanding of Kubernetes internals and experience running GPU workloads in production environments
• In-depth knowledge of AWS services
• Experience architecting systems that support both real-time and asynchronous processing pipelines
• Familiarity with the ML lifecycle and MLOps practices, including CI/CD for models, monitoring, and rollback strategies
  
  
  
  

Bonus Qualifications

• Experience working in research-driven environments or alongside data scientists, algorithm research team and ML engineers
• Contributions to open-source projects related to model serving, Kubernetes operators, or ML platforms
• Experience supporting systems with diverse user groups across engineering and research disciplines
  
  
  
  

Why Join Us?

• Opportunity to build and scale a one-of-a-kind platform powering state-of-the-art media algorithms
• Collaborate with world-class research, engineering, and product teams
• Have a direct impact on product experiences used by millions of developers and end-users
• Be part of a culture that values creativity, autonomy, and continuous improvement
  
  
  
  

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.