Senior Applied Scientist

About the Role

We are hiring an Applied Scientist to help advance personalization across the Crunchyroll ecosystem. In this role, you will lead the scientific development of recommendation, ranking, and decisioning solutions that improve how fans discover and engage with anime series/movies, manga, merchandise, games, and other areas in the anime fandom. You will partner closely with Machine Learning Engineers, Product, Engineering, Marketing, and Content stakeholders to make Crunchyroll the ultimate destination for anime experience.

Responsibilities

• Lead the research and development of recommendation, ranking, retrieval, and personalization methods tailored to Crunchyroll use cases across streaming, manga, ecommerce, and lifecycle marketing surfaces.
• Frame ambiguous business and product questions into clear scientific problems, hypotheses, success metrics, and experimentation plans.
• Design and run robust offline evaluation frameworks for recommender systems, including relevance, diversity, novelty, coverage, calibration, and long-term value metrics.
• Partner with Product, Analytics, and Engineering to define online experiments, interpret results, and turn learnings into roadmap decisions and model improvements.
• Develop user, content, and contextual understanding through feature design, representation learning, segmentation, and behavioral analysis.
• Prototype and evaluate a range of approaches, including collaborative filtering, content-based methods, sequence modeling, deep learning, bandits, causal or uplift methods, and LLM-enabled recommendation techniques where appropriate.
• Analyze user feedback loops and cross-domain interactions to improve discovery across video, merchandise, manga, and other ecosystem experiences.
• Work closely with Machine Learning Engineers to translate promising research into production-ready solutions on our in-house recommendation platform.
• Communicate scientific findings, model tradeoffs, and business implications clearly to technical and non-technical stakeholders.
• Help establish best practices for experimentation, reproducibility, model governance, and scientific documentation within the personalization and recommendations function.

Requirements

• 5+ years of experience in applied machine learning, recommendation systems, search/ranking, experimentation, or a closely related area, with a track record of driving measurable product impact.
• Strong foundations in machine learning, statistics, experimental design, and causal thinking.
• Hands-on experience with collaborative filtering, retrieval and ranking systems, representation learning, sequence / generative models, bandits, graph methods, or personalization for consumer products.
• High proficiency in Python and familiarity with ML libraries such as PyTorch, TensorFlow, Scikit-learn, XGBoost, or similar tooling.
• Experience with SQL, distributed data processing, and cloud-based ML workflows is strongly preferred.
• Ability to design offline and online evaluations, reason about metrics, and connect experimental findings to user and business outcomes.
• Experience partnering with engineers, product managers, analysts, marketers, and business stakeholders.
• Strong communication skills to explain complex modeling decisions and findings to diverse audiences.
• MS or PhD in Computer Science, Machine Learning, Statistics, Operations Research, Economics, or a related quantitative discipline, or equivalent applied industry experience.

Nice to Have

• Experience personalizing content, commerce, media, entertainment, gaming, or subscription products at scale.
• Familiarity with recommender-system failure modes such as popularity bias, cold start, sparse feedback, and feedback loop effects.
• Experience with multi-objective optimization, constrained ranking, or balancing short-term engagement with long-term user value.
• Exposure to generative AI, representation learning, or LLM applications that support recommendation and personalization workflows.
• Published research, patents, or open-source contributions in recommendation systems, personalization, applied machine learning, or experimentation.

Benefits

• Receive a great compensation package including salary plus performance bonus earning potential, paid annually.
• Flexible time off policies allowing you to take the time you need to be your whole self.
• Generous medical, dental, vision, STD, LTD, and life insurance.
• Health Saving 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 and pet-friendly offices in some locations.

Pay

The Pay Range for this position is $185,000—$230,000 USD in Los Angeles, CA, and $205,000—$245,000 USD in San Francisco, CA. Actual pay will vary based on factors including location, experience, and performance.

Schedule

This position is based in our Los Angeles office, with a hybrid schedule requiring in-office presence on Tuesday, Wednesday, and Thursday. San Francisco office is a secondary location.