MLOps engineer

For many of us there’s that one podcast we never miss, and video content is part of our daily routine, whether it’s professional or personal. But how many of us truly understand the effort that goes on behind the scenes? Here at Riverside, we know it well. That’s exactly why we built an AI-powered platform that helps content creators, podcasters, marketeers, and more at major brands like Netflix, Disney, Google, and Microsoft to create high-quality content with ease.

Riverside’s technology streamlines the entire content creation process, turning ideas into professional-grade content with the highest production standards, without requiring expensive equipment or external services. The secret? AI-driven tools that replace traditional production roles like editing, directing, and design, automating the entire process at the click of a button.

About The Innovation Team

At Riverside, the Innovation team is all about pushing the limits of AI to transform content creation. We work on everything from enhancing audio and video quality to automating production with machine learning, NLP, and computer vision. Whether it’s real-time rendering, intelligent editing, or new ways to process media, we turn cutting-edge research into practical tools that creators rely on. If you’re excited about shaping the future of media tech, you’ll fit right in.

On your day to day

We are looking for a Backend Engineer to join our Machine Learning team and help build the infrastructure that powers cutting-edge AI models in Natural Language Processing (NLP) and Computer Vision (CV). In this role, you'll collaborate closely with machine learning and video-processing teams to shape the AI-driven future of our product.

What Will Make You Stand Out?

• 6+ years of production-grade Python development experience
• Strong background in designing, developing, and debugging complex distributed systems (e.g., microservices, event-driven architectures).
• Hands-on experience with containerized environments, microservices, and Kubernetes.
• Experience with workflow management frameworks (Airflow,Temporal) and asynchronous programming.
• Familiarity with machine learning concepts and frameworks (PyTorch) for effective collaboration.
• Experience with cloud platforms (AWS, Azure, or GCP) and model-serving frameworks (Triton, BentoML, Ray Serve).
• Passion for AI, automation, and optimizing ML Services at scale!