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 MLOps team and help build the infrastructure that powers cutting-edge AI models. In this role, you’ll manage the end-to-end MLOps lifecycle, designing event-driven systems that handle massive video data and moving compute-intensive, generative models from research to production. You'll collaborate closely with AI researchers and video-processing teams to ensure our AI services are scalable, reliable, and performant.

What Will Make You Stand Out?

• 6+ years of production-grade Python development experience.
• Strong background in distributed systems: You’ve built and debugged complex, event-driven architectures (e.g., Kafka, microservices).
• Expertise in Data Engineering at scale: Experience building massive data pipelines and architecting Data Lakes (S3) with compute layers like Athena for large-scale analysis.
• Deep understanding of the MLOps lifecycle: Experience taking models from training to deployment, including versioning and performance monitoring.
• Experience with containerized environments, microservices, and Kubernetes.
• Experience with workflow management frameworks (Temporal, Airflow) and asynchronous programming.
• Experience with cloud platforms (AWS preferred) and model-serving frameworks (Triton, VLLM/SGLang, Ray Serve).
• A love for exploring new tech and the drive to implement modern frameworks that move the needle.