Senior MLOps Engineer

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.

About You

We get excited about candidates, like you, because...

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or a related field.
• 8+ years of experience in MLOps, ML infrastructure, or DevOps for AI/ML systems.
• MLflow, SageMaker, Databricks ML for experiment tracking, model registry, and lifecycle management.
• Airflow, or Step Functions for workflow orchestration.
• MLFLow for monitoring ML models in production.
• Deep knowledge of CI/CD and automation frameworks (GitHub Actions, Terraform, CloudFormation).
• Hands-on experience with containerization (Docker) and orchestration (EKS).
• Proficiency in Python and scripting for ML integrations.
• Strong knowledge of cloud platforms (AWS preferred) and services relevant to ML (SageMaker, Lambda, S3, Kinesis, Step Functions).
• Understanding of security, compliance, and governance in ML production systems.
• Excellent problem-solving and communication skills, with a proven ability to work with cross-functional teams of data scientists

About the Team

The R&D team