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About the Role

As an Applied Scientist for Recommendation and Personalization, you will report to the Director of Data Science and Machine Learning. You will own the research and applied science agenda for personalization, from problem framing and data exploration through model development, evaluation, experimentation, and iteration. This role is ideal for someone who enjoys combining strong scientific rigor with product thinking to improve user discovery, engagement, retention, and long-term fan value. You will work across multiple user touchpoints, including app and web interfaces, lifecycle and promotional email campaigns, and flywheels that connect video, ecommerce, manga, and adjacent experiences. You will help define what great personalization looks like at Crunchyroll, build the evidence to prove impact, and collaborate with engineering partners to ensure the resulting solutions can be productionized effectively.

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 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.
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.

Skills

Python
PyTorch
TensorFlow
Scikit-learn
XGBoost
SQL
Distributed data processing
Cloud-based ML workflows
Collaborative filtering
Retrieval and ranking systems
Representation learning
Sequence models
Generative models
Bandits
Graph methods
Personalization
Machine learning
Statistics
Experimental design
Causal thinking
Recommendation systems
Search/Ranking
Experimentation

Location

Los Angeles, CA
San Francisco, CA

Work Type

Hybrid

Experience Level

Senior
5+ years

Education Level

MS or PhD in Computer Science, Machine Learning, Statistics, Operations Research, Economics, or a related quantitative discipline, or equivalent applied industry experience.

Salary/Compensations

$185,000—$230,000 USD (Los Angeles, CA)
$205,000—$245,000 USD (San Francisco, CA)

Benefits

Salary plus performance bonus earning potential
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

About the Company

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.
Join our team, and help us shape the future of anime!
Crunchyroll, LLC is an independently operated joint venture between US-based Sony Pictures Entertainment, and Japan's Aniplex, a subsidiary of Sony Music Entertainment (Japan) Inc., both subsidiaries of Tokyo-based Sony Group Corporation.

Equal Opportunity

We are an equal opportunity employer and value diversity at Crunchyroll.
Pursuant to applicable law, we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.