Senior Machine Learning Engineer, Recommendation and Personalization

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, connecting them with the stories and characters they crave through streaming video, theatrical releases, games, merchandise, events, and more.

About the role

As Senior Machine Learning Engineer for Recommendation and Personalization, you will report to the Director of Data Science and Machine Learning in the Center for Data and Insights. Based in the Los Angeles or San Francisco area, you will lead the research, development, and testing of advanced models, collaborating with product and engineering teams to deliver tailored experiences for fans across the anime ecosystem—including video recommendations, digital manga suggestions, merchandise personalization, gaming, music, and more.

This hybrid role requires in-office work three days a week (Tuesday, Wednesday, Thursday) at our Los Angeles or San Francisco offices.

Responsibilities

• Research, design, and implement machine learning algorithms for recommendation systems, including collaborative filtering, content-based models, deep learning, and generative recommendation approaches to personalize content discovery.
• Co-develop end-to-end ML pipelines for data ingestion, feature engineering, model training, evaluation, and deployment using scalable cloud platforms.
• Optimize models for accuracy, latency, and scalability to handle massive user interaction data from streaming and multi-platform, multi-domain experiences.
• Integrate personalization solutions with other services, implementing monitoring for model performance, bias detection, and automated retraining.
• Collaborate on A/B testing, experimentation, and iterative improvements to refine recommendations based on user feedback and evolving content trends.

Requirements

• 8+ years of hands-on experience in applied machine learning, with a proven track record in building recommendation systems or personalization engines, ideally in media, entertainment, or e-commerce.
• Expertise in Python and frameworks like TensorFlow, PyTorch, or Scikit-learn, with proficiency in MLOps tools such as MLflow, Docker, and cloud services like AWS SageMaker or Databricks.
• Experience with big data tools (e.g., Spark) and cloud infrastructure (e.g., AWS/GCP) for handling large-scale datasets.
• Ability to partner with data scientists, engineers, and product teams to deploy models aligned with business objectives like increasing user retention and content consumption.
• Strong communication skills to document research findings, explain algorithmic choices, and present results to diverse stakeholders.

Qualifications

• Graduate degree (MS or PhD) in Computer Science, Machine Learning, Statistics, or a related quantitative field.
• Publications or contributions in recommendation systems are a plus.

About the team

Our team consists of passionate Machine Learning Engineers and Data Scientists who have driven significant impact across product offerings, content strategy, and user engagement. As we expand, we aim to become the cornerstone of innovation and growth across Crunchyroll’s business verticals, leveraging advanced machine learning to transform how users interact with and enjoy our content and services.

Benefits

• Competitive compensation package including salary plus performance bonus.
• Flexible time off policies.
• Comprehensive medical, dental, vision, STD, LTD, and life insurance.
• Health Savings Account (HSA) program.
• Health care and dependent care FSA.
• 401(k) plan with employer match.
• Employer-paid commuter benefit.
• Support program for new parents.
• Pet insurance; some offices are pet-friendly.

Pay

Los Angeles, CA: $185,000—$225,000 USD

San Francisco, CA: $205,000—$240,000 USD

Actual pay varies based on location, experience, and performance. The range is one component of Crunchyroll’s Total Rewards, which may also include performance bonuses, retirement savings matching, time-off programs, and health benefits.

Schedule

Hybrid schedule: in-office three days a week (Tuesday, Wednesday, Thursday).