Senior Machine Learning Engineer, Fraud Detection

About Crunchyroll

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, and help them connect with the stories and characters they crave.

About the Role

In the role of Senior Machine Learning Engineer, you will report to the Director of Data Science and Machine Learning in the Center for Data and Insights, working from Los Angeles or San Francisco area in California.

In this role focused on Algorithm Development and End-to-End Production, you will lead the creation and deployment of scalable ML models to detect account sharing fraud, drawing from user behavior data like geolocation, concurrent sessions, and device profiles. This position collaborates closely with data scientists, product analysts, and data engineers to translate research prototypes into production systems.

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

Core Areas of Responsibility

• Design and implement machine learning algorithms, including anomaly detection models, to identify unauthorized account sharing in real-time.
• Develop end-to-end ML pipelines for data preprocessing, model training, evaluation, and deployment using cloud platforms.
• Optimize models for performance, scalability, and efficiency to handle high-volume streaming data.
• Integrate ML solutions with existing systems via APIs and establish monitoring for model drift and retraining.
• Collaborate on A/B testing and iteration to refine algorithms based on feedback and evolving evasion tactics.

Qualifications

• Experience: 8+ years of hands-on experience in ML engineering, with a track record in fraud or anomaly detection systems, ideally in online, consumer-facing products and services.
• Technical Skills: Expert in Python and frameworks like TensorFlow, PyTorch, or XGBoost, with proficiency in MLOps tools such as MLflow, Docker, and cloud services like AWS SageMaker, Databricks, etc. Experience with cloud computing infrastructure and tooling such as AWS/GCP.
• Cross-Functional Collaborations: Experienced in working with data scientists, engineers, and product teams to deploy models supporting business goals like revenue optimization.
• Communication Skills: Strong verbal and written communication skills to document technical processes and explain algorithm decisions to diverse stakeholders.
• Education: Graduate degree (MS or PhD) in Computer Science, Statistics, Machine Learning, or a related quantitative field. Certifications in cloud ML are a plus.