Data Scientist Engineer III

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.

• Research, develop, and deploy Graph Neural Network (GNN) models, GraphRAG pipelines, and LLM-based solutions for real-world applications.
• Design and implement fraud detection models leveraging machine learning, NLP, and graph-based techniques.
• Build and optimize interactive content discovery systems using Vector Databases and embedding-based techniques.
• Develop and maintain end-to-end ML pipelines from data ingestion to model deployment and monitoring.
• Work with AWS cloud services (e.g., SageMaker, Databricks, Lambda, S3, Kinesis) to scale AI/ML workflows.
• Optimize models for efficiency, performance, storage, and cost, ensuring they drive measurable business impact (e.g., content discovery, fraud prevention).
• Collaborate with data engineering and software teams to ensure smooth deployment, monitoring, and scalability of AI/ML models in production.
• Communicate technical concepts effectively to both technical and non-technical stakeholders.

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

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or a related field.
• 5+ years of experience in data science, with a strong focus on machine learning, AI, and production-grade model deployment.
• Strong expertise in AI/ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Hands-on experience with Graph Neural Networks, GraphRAG, LLMs, Graph and Vector Databases.
• Experience with fraud analysis using machine learning, graph analytics, or NLP techniques.
• Proficiency in Python, R, or similar programming languages.
• Strong knowledge of cloud platforms (AWS preferred) and experience with data-related services (e.g., Databricks, SageMaker, Kinesis, Lambda).
• Ability to optimize AI/ML models for performance, efficiency, and cost-effectiveness.
• Strong analytical, problem-solving, and communication skills, with the ability to explain complex ML concepts to cross-functional stakeholders.

The R&D 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.