Senior Applied Scientist

Crunchyroll, LLC•Los Angeles, CA

1d•$185,000 - $245,000•Hybrid

About The Position

Crunchyroll is seeking a Senior Applied Scientist to advance personalization across its ecosystem. This role involves leading the scientific development of recommendation, ranking, and decisioning solutions to enhance how fans discover and engage with anime series, movies, manga, merchandise, and games. The scientist will collaborate with Machine Learning Engineers, Product, Engineering, Marketing, and Content stakeholders to establish Crunchyroll as the premier destination for anime experiences. The position reports to the Director of Data Science and Machine Learning and is based in Los Angeles, with San Francisco as a secondary location. The role operates on a hybrid schedule, requiring three days a week in the office.

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 at least some of the following: collaborative filtering, retrieval and ranking systems, representation learning, sequence / generative models, bandits, graph methods, or personalization for consumer products.
Highly proficient in Python and comfortable working with common ML libraries such as PyTorch, TensorFlow, Scikit-learn, XGBoost, or similar tooling.
Experience working with SQL, distributed data processing, and cloud-based ML workflows is strongly preferred.
Ability to design offline and online evaluations, reason carefully about metrics, and connect experimental findings to user and business outcomes.
Experience partnering effectively with engineers, product managers, analysts, marketers, and business stakeholders to move from idea to execution.
Ability to explain sophisticated modeling decisions and ambiguous findings in a clear, decision-oriented way 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 Haves

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.

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.

Benefits

Great compensation package including salary plus performance bonus earning potential, paid annually.
Flexible time off policies.
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.
Some offices are pet friendly.