<div class="content-intro"><h2 data-pm-slice="1 1 []">About Crunchyroll</h2> <p>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. Whether that experience is online or in-person, streaming video, theatrical, games, merchandise, events and more, it’s powered by the anime content we all love.</p> <p>Join our team, and help us shape the future of anime!</p></div><h2 data-pm-slice="1 1 []"><strong>About the role</strong></h2> <p><span class="hl-blue-solid ai_replace" data-highlight-id="d8870ac6ca208eef30e4da568ba08865-0">In the role of 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, working from Los Angeles or San Francisco area in California.</span> <span class="hl-blue-solid ai_replace" data-highlight-id="b72c519502385d208ebf2328d40bf109-0">You will lead the research, development, and test of advanced models, and work together with product and engineering partners to deliver tailored experiences for our fans across the anime ecosystem, including anime video recommendations, digital manga suggestions, merchandise personalization, anime-themed gaming, music, and more.</span> <span class="hl-blue-solid ai_replace" data-highlight-id="65816c50332221b73a8271ced1fcb0f7-0">This position will collaborate closely with scientists, engineers, product owners, to prototype innovative algorithms, evaluate their impact, and integrate them into production systems that drive user engagement, retention, discovery, and satisfaction.<br><br>We work a hybrid schedule, in-office three days a week; Tuesday, Wednesday, Thursday. This position can be based in our Los Angeles or San Francisco offices.</span></p> <p><strong>Core <span class="hl-blue-solid replace" data-highlight-id="e650eec2d8d8472a918249e6e8a9af7e-0">Areas of Responsibility</span></strong></p> <ul> <li><span class="hl-blue-solid replace" data-highlight-id="35db324e3ef4c32ec1b92b0c8705bc6f-0">Research, design</span>, and implement machine learning algorithms for recommendation systems, including collaborative filtering, content-based models, and deep learning, and generative recommendation approaches to personalize content discoveries.</li> <li>Co-develop end-to-end ML pipelines for data ingestion, feature engineering, model training, evaluation, and deployment using scalable cloud platforms with engineers.</li> <li><span class="hl-blue-solid replace_or_delete" data-highlight-id="c971c2f7659fe68c424b128c20fb14c6-0">Optimize</span> models for accuracy, latency, and scalability to handle massive user interaction data from streaming and multi-platform, multi-domain experiences across the fandom.</li> <li>Integrate personalization solutions with other services, and implement monitoring for model performance, bias detection, and automated retraining.</li> <li>Collaborate on A/B testing, experimentation, and iterative improvements to refine recommendations based on user feedback and evolving content trends.</li> </ul> <p><strong>About You</strong></p> <p>We get excited about candidates, like you, because you have:</p> <p><strong><span class="hl-blue-solid ai_replace" data-highlight-id="e6ec73e4765644053c35abf7e5943a42-0">Experience:</span></strong><span class="hl-blue-solid ai_replace" data-highlight-id="e6ec73e4765644053c35abf7e5943a42-0"> You bring 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 platforms.</span></p> <p><strong><span class="hl-blue-solid ai_replace" data-highlight-id="21a73bc464024e03e1cd2cf052f09457-0">Technical Skills:</span></strong><span class="hl-blue-solid ai_replace" data-highlight-id="21a73bc464024e03e1cd2cf052f09457-0"> Expert 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, Databricks, or similar.</span> Experience with big data tools (e.g., Spark) and cloud infrastructure (e.g., AWS/GCP) for handling large-scale datasets.</p> <p><strong><span class="hl-blue-solid replace_or_delete" data-highlight-id="3680cc8314b035fa7128ef59cffe2113-0">Cross-Functional</span> Collaborations:</strong> Experienced in partnering with data scientists and analysts, engineers, and product teams to deploy models that align with <span class="hl-blue-solid replace" data-highlight-id="6112fb4c6b66f834a5588ee42e2c7b9c-0">business objectives</span> like increasing user retention and content <span class="hl-blue-solid replace" data-highlight-id="3811a80ddbb8eb756c86bd8dedc061ea-0">consumption</span>.</p> <p><strong><span class="hl-red-solid replace_or_delete" data-highlight-id="c75ea7ab6c497e689ce7f3fe689c7563-0">Communication Skills</span>:</strong> <span class="hl-blue-solid replace_or_delete" data-highlight-id="6c0d963d2e4686c2de64d81fa921282d-0">Strong</span> ability to document research findings, explain algorithmic choices, and present results to diverse <span class="hl-blue-solid replace_or_delete" data-highlight-id="739cd8ee1c8fb2bbf130b1077303cd4b-0">stakeholders</span> for <span class="hl-blue-solid replace_or_delete" data-highlight-id="ec1ce7c82f91cc09efa213773111a49f-0">effective</span> adoption.</p> <p><strong>Educational Background:</strong> Graduate degree (MS or <span class="hl-red-solid delete" data-highlight-id="bae04d98615c1223cc0161ba19e155d6-0">PhD</span>) in Computer Science, Machine Learning, Statistics, or a related quantitative field, with publications or contributions in recommendation systems being <span class="hl-red-solid delete" data-highlight-id="323f36660174059b5b76461e4d6d8887-0">a plus</span>.</p> <h2><strong>About the Team</strong></h2>