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.
- Lead and mentor a team of data scientists focused on developing and deploying ML solutions including GNNs, LLM-based applications, and fraud detection models.
- Define and drive the roadmap for AI/ML initiatives across Crunchyroll’s product and platform ecosystem.
- Guide the development of robust ML pipelines and infrastructure, working closely with data engineering, software engineering, and DevOps teams.
- Establish best practices for experimentation, model evaluation, A/B testing, and continuous learning in production environments.
- Work with stakeholders across business, marketing, security, and content teams to identify AI opportunities and translate them into actionable projects.
- Ensure responsible AI/ML development by emphasizing model interpretability, performance, and fairness.
- Own operational metrics around model impact, latency, cost, and system scalability.
- Foster a strong team culture centered on innovation, curiosity, collaboration, and continuous learning.
About You
We get excited about candidates, like you, because...
- Bachelor's or Master's in Computer Science, Statistics, Data Science, or a related quantitative field.
- 8+ years of industry experience in data science or applied ML, with 4+ years of people leadership experience.
- Proven track record of delivering production ML systems that solve real-world business problems.
- Deep knowledge of ML/AI frameworks (e.g., PyTorch, TensorFlow, HuggingFace) and experience with applied NLP, GNNs, GraphRAG, and embeddings.
- Experience building systems leveraging Graph Databases, Vector Stores (e.g., FAISS, Pinecone), and cloud-native ML stacks (SageMaker, Databricks, etc.).
- Strong technical leadership, with the ability to direct teams to develop optimal ML solutions and guide research-to-production lifecycle.
- Excellent communication and stakeholder management skills; able to translate between business and technical worlds.
- A passion for building high-performing teams, mentoring others, and fostering a learning-first environment.