MLOps Engineer

About Crunchyroll

Founded by fans, Crunchyroll delivers the art and culture of anime to a passionate community. We super-serve over 100 million anime and manga fans across 200+ countries and territories, and help them connect with the stories and characters they crave. Whether that experience is online or in-person, streaming video, theatrical, games, merchandise, events and more, it’s powered by the anime content we all love.

Join our team, and help us shape the future of anime!

About the role

Crunchyroll is growing and changing, presenting unique challenges and opportunities to support millions of anime fans around the world. The AI/ML team provides seamless help to our internal stakeholders, ensuring an exceptional experience for all Crunchyroll fans. The AI/ML team relies on strong MLOps practices to ensure models are reliable, scalable, and impactful in production.

• Design, build, and maintain end-to-end ML infrastructure and pipelines to support model training, deployment, and monitoring.
• Develop and manage CI/CD pipelines for ML to enable fast, reliable, and automated delivery of ML models.
• Implement and manage model registry, experiment tracking, and versioning using tools like MLflow, SageMaker Model Registry, or equivalent.
• Establish monitoring, observability, and alerting frameworks to detect drift, degradation, and anomalies in real-time.
• Partner with data scientists to productionize ML models, ensuring seamless transition from research to production.
• Optimize ML workflows for performance, scalability, and cost-effectiveness across training and inference.
• Leverage platforms such as AWS SageMaker, Databricks, Kinesis, Lambda, Kubernetes (EKS), and Docker for ML operations.
• Collaborate with data engineering and software engineering teams to integrate ML services into large-scale distributed systems.
• Drive best practices for MLOps, including reproducibility, governance, compliance, and security of deployed models.

How you’ll work with Data Science

• Partner with ML Engineers to deploy and scale models built with frameworks like PyTorch, TensorFlow, and Scikit-learn.
• Help data scientists track experiments, compare runs, and promote models to production.
• Translate research notebooks into production-grade pipelines with reproducible training and inference workflows.
• Co-own model lifecycle management: data, training, validation, deployment, monitoring, retraining.
• Ensure ML models align with software engineering best practices for testing, automation, and observability.