Senior Machine Learning Engineer, Fraud Detection

Crunchyroll; San Francisco (CA); On-site; USD 185,000 - 240,000; Full time

Benefits offered by this job

Salary plus performance bonus

Flexible time off

Medical, dental, vision

HSA and FSA

401(k) with employer match

Commuter benefit

New parent support

Pet insurance

Job summary

Crunchyroll is seeking a Senior Machine Learning Engineer focused on fraud detection to design and deploy scalable models that identify unauthorized account sharing in real time. You will work from the Los Angeles or San Francisco area, reporting to the director of data science within the Center for Data and Insights.

The role emphasizes end‑to‑end ML pipelines, collaboration with data scientists, product analysts, and data engineers, and deployment of production systems with robust monitoring

Qualifications

• Graduate degree in CS, ML or related quantitative field.
• 5+ years hands-on ML engineering experience, fraud/ anomaly detection preferred.
• Experience deploying production ML systems and collaborating with cross‑functional teams.

Responsibilities

• Design and implement real-time fraud detection ML algorithms.
• Build end‑to‑end ML pipelines for data processing, training, evaluation and deployment.
• Optimize models for performance and scalability on high-volume streaming data.
• Integrate ML solutions with existing systems via APIs and monitor drift.
• Collaborate on A/B testing and iterate against evolving evasion tactics.

Skills

Python

TensorFlow

PyTorch

XGBoost

MLflow

Docker

AWS SageMaker

Databricks MLFlow

GCP

Education

Master's degree in Computer Science or related field

Tools

MLflow

Docker

AWS SageMaker

Databricks MLFlow

Job description

Senior Machine Learning Engineer, Fraud Detection

Crunchyroll | Full-time | Negotiable | Master (>10 yrs) | Posted Mar 1

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 Machine Learning Engineer role focused on Algorithm Development and End-to-End Production at our VOD streaming company, 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 is essential for collaborating with data scientists, product analysts, and data engineers, translating research prototypes into production systems that enhance detection accuracy while minimizing user disruption, aligning with proven strategies from streaming giants to boost subscriber growth.

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.

About You

• Experience: You bring 5+ 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 MLFlow, etc. Experience with cloud computing infra and tooling such as AWS/GCP.
• Cross‑Functional Collaborations: Experienced in working with data scientists, engineers, and product teams to deploy models that support business goals like revenue optimization.
• Communication Skills: Strong ability to document technical processes and explain algorithm decisions to diverse stakeholders for seamless integration.
• Education Background: Possess a master's degree in computer science, machine learning, or a related quantitative field, with certifications in cloud ML being a plus.
• Communication Skills: Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non‑technical stakeholders.

Educational Background

Graduate degree (MS or PhD) in Computer Science, Statistics, or a related field.

About The Team

Our team is composed of passionate Machine Learning Engineers and Data Scientists who have already made a significant impact across our product offerings, content strategy, and user engagement metrics. As we expand our scope, our team is poised to become the cornerstone of innovation and growth across various business verticals within the company.

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 Flexible Spending Account (FSA).
• 401(k) plan, with employer match.
• Employer paid commuter benefit.
• Support program for new parents.
• Pet insurance and some of our offices are pet friendly.

Pay

• Los Angeles, CA: $185,000-$225,000 USD
• San Francisco, CA: $205,000-$240,000 USD

About Our Values

• Courage. We believe that when we overcome fear, we enable our best selves.
• Curiosity. We are curious, which is the gateway to empathy, inclusion, and understanding.
• Kaizen. We have a growth mindset committed to constant forward progress.
• Service. We serve our community with humility, enabling joy and belonging for others.

Equal Opportunity Employer

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.