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 RD team is dedicated to developing, testing, and validating robust and scalable machine learning models that drive business objectives. Our focus includes enhancing operational processes through AI/ML solutions, such as trend analysis, anomaly detection, and the deployment of large language models (LLMs) for tasks like querying system health. Another major focus area is preserving, and improving customer experience and retention. We closely work with our stakeholders to ensure AI/ML objectives are clearly defined.

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

Senior MLOps Engineer

Job Description

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