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Fraud Detection Machine Learning Jobs (NOW HIRING)

ID.me

Senior Fraud Investigations Analyst

Mclean, VA · On-site

Use fraud detection tools, machine learning outputs, and risk-scoring systems to drive high-quality investigations * Independently own investigations from detection through resolution, including ...

ID.me

Senior Fraud Investigations Analyst

Mclean, VA · On-site

... detection tools, machine learning outputs, and risk-scoring systems to drive high-quality ... fraud cases • Develop and refine investigation playbooks and analytical approaches for ATO ...

Appgate

Staff Machine Learning Engineer

Manhattan, NY · On-site

$180K - $220K/yr

You will architect large-scale ML systems that detect and prevent fraud in real time combining deep machine learning expertise with scalable engineering and domain knowledge in financial systems.

Appgate

Principal Machine Learning Engineer

Manhattan, NY · On-site

$220K - $265K/yr

You will architect large-scale ML systems that detect and prevent fraud in real time combining deep machine learning expertise with scalable engineering and domain knowledge in financial systems.

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How much do fraud detection machine learning jobs pay per hour?

As of Aug 12, 2026, the average hourly pay for fraud detection machine learning in the United States is $18.05, according to ZipRecruiter salary data. Most workers in this role earn between $14.90 and $19.23 per hour, depending on experience, location, and employer.

What are some common challenges faced by professionals working in fraud detection machine learning, and how can they be addressed?

Professionals in Fraud Detection Machine Learning often face challenges such as dealing with highly imbalanced datasets, rapidly evolving fraud patterns, and the need for real-time detection. Managing data imbalance requires careful selection of evaluation metrics and specialized algorithms. Staying ahead of new fraud tactics involves continuous model retraining and close collaboration with domain experts. Additionally, integrating machine learning solutions with existing systems often requires cross-functional teamwork with IT, security, and compliance teams.

What is fraud detection using machine learning?

Fraud detection using machine learning involves leveraging algorithms and data analysis techniques to identify suspicious or fraudulent activities in various domains, such as banking, e-commerce, or insurance. These systems analyze large volumes of transaction data to detect patterns or anomalies that may indicate fraud. Machine learning models can adapt over time, improving their accuracy as they are exposed to more data. This approach helps organizations automate and enhance their ability to prevent, detect, and respond to fraudulent behavior efficiently.

What is the difference between Fraud Detection Machine Learning vs Fraud Analyst?

AspectFraud Detection Machine LearningFraud Analyst
CredentialsData science, machine learning certifications, programming skillsFinance, criminal justice degrees, analytical skills
Work EnvironmentData-driven, tech-focused, often in financial or e-commerce sectorsInvestigative, report-focused, in financial institutions or insurance companies
Employer & IndustryTech companies, banks, e-commerce platformsFinancial institutions, insurance firms, retail

Fraud Detection Machine Learning involves developing algorithms to identify fraudulent activities automatically, relying heavily on data analysis and programming. Fraud Analysts manually investigate suspicious cases and interpret data insights. While both roles aim to prevent fraud, Machine Learning specialists focus on building models, whereas Fraud Analysts focus on case investigation and decision-making.

What are the key skills and qualifications needed to thrive as a fraud detection machine learning specialist, and why are they important?

To thrive as a Fraud Detection Machine Learning Specialist, you need strong expertise in machine learning, statistical analysis, and programming languages like Python or R, typically supported by a degree in computer science, data science, or a related field. Familiarity with tools such as TensorFlow, Scikit-learn, SQL databases, and experience with big data platforms or cloud services is highly valuable. Critical thinking, attention to detail, and effective communication are crucial soft skills for identifying complex fraud patterns and collaborating with interdisciplinary teams. These competencies are vital for developing accurate models that protect organizations from financial losses and maintain trust with customers.
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What cities are hiring for Fraud Detection Machine Learning jobs? Cities with the most Fraud Detection Machine Learning job openings:
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What job categories do people searching Fraud Detection Machine Learning jobs look for? The top searched job categories for Fraud Detection Machine Learning jobs are:
Fraud Detection Machine Learning jobs near you
Infographic showing various Fraud Detection Machine Learning job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $37,548 per year, or $18.1 per hour.

Senior Machine Learning Engineer, Fraud Detection

Crunchyroll, LLC

Los Angeles, CA • Hybrid

$112K - $154K/yr

Full-time

Medical, Dental, Vision, Life, Retirement

Posted yesterday

New

Job description

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.
We work a hybrid schedule, in-office three days a week; Tuesday, Wednesday, and Thursday. 

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 8+ 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. We are dedicated to leveraging advanced machine learning techniques to continue transforming how users interact with and enjoy our offering of video contents and all other services.

Why you will love working at Crunchyroll

In addition to getting to work with fun, passionate and inspired colleagues, you will also enjoy the following benefits and perks:

  • 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 some of our offices are pet friendly!

#LifeAtCrunchyroll #LI-Hybrid

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