Backend Engineer - ML Team

About Riverside

We’re a virtual studio that makes recording and editing at the highest quality possible, accessible to anyone. Since launching, we have experienced rapid growth and are trusted by customers such as Spotify, NY Times, NBC, CNN and iHeartMedia.

About the role

As a Backend Engineer within our machine learning team, you will play a crucial role in developing and maintaining the infrastructure and systems that power our machine learning models in both NLP and CV domains. You will work closely with machine-learning and video-processing teams to pave the AI-based vision of our product.

Key Responsibilities:

• Backend Development: Design, develop, and maintain robust, scalable, and high-performance backend systems that support machine learning workflows. This includes building APIs and stream services, which involve complex data workflows.
• Performance Optimization: Identify and address bottlenecks in the backend systems to ensure optimal performance and scalability. Conduct thorough testing, profiling, and monitoring of the system to improve efficiency and reliability.
• Research and Innovation: Stay updated with the latest advancements in backend technologies, frameworks, and tools related to machine learning. Continuously explore and evaluate new approaches to improve the efficiency and effectiveness of our machine learning infrastructure.

Qualifications and Skills:

• Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.
• Good familiarity with machine learning concepts and frameworks (e.g. PyTorch) for effective collaboration.
• At least 6 years of experience with production-grade Python development. Experience with C++ is a plus.
• Experience with workflow-management frameworks (Airflow, Luigi, Temporal) and asynchronous programming.
• Solid understanding of backend development concepts, including API design, microservices, data modeling and design patterns.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Experience with frameworks for serving models (Triton, TorchServe) or MLOps platforms is a plus.