Machine Learning Engineer

Job Summary We are seeking a talented Senior Machine Learning Engineer to join our team and focus on building advanced fraud prediction models. This role involves developing core decision models for various aspects of fraud prevention, including identity, onboarding, authentication, abuse, scam and product‑specific models. The ideal candidate will leverage anomaly detection, supervised learning, and experiential learning techniques to create robust and effective fraud prevention solutions.

Responsibilities Design and implement core decision models for identity, onboarding, authentication, abuse, scam, and product‑specific fraud prevention. Develop and refine anomaly detection algorithms to identify potential fraud patterns. Apply supervised learning techniques to build predictive models that accurately identify fraudulent activities.

Utilize continual learning methods to continuously improve model performance and adapt to new fraud tactics. Collaborate with cross‑functional teams to integrate fraud prediction models into systems and processes. Conduct experiments, analyze results, and interpret findings to drive innovation and enhance decision‑making.

Ensure data integrity and consistency by working closely with business stakeholders and engineers. Advocate for a data‑driven culture and best practices in data science and fraud prevention. Requirements Master’s degree or PhD in Computer Science, Statistics, Data Science, Machine Learning, Artificial Intelligence, or a related quantitative field. 5+ years of experience in Data Science, ML Engineering, or AI Research roles, with a track record of building and deploying real‑world predictive models.

Strong understanding of anomaly detection, supervised learning techniques, and experiential learning methods; experience in fraud prevention is a plus. Excellent interpersonal, written, and verbal communication skills, and experience collaborating across multiple business functions. Preferred Qualifications Familiarity with decision models for identity and authentication.

Experience in fraud prevention and detection. Experience driving data instrumentation for experimentation and large‑scale data collection. Familiarity with building systems that incorporate real‑time feedback and continuous learning.

Knowledge of reinforcement learning, contextual bandits, sequence models, optimization, or graph mining. Travel 0% travel. This role is fully remote.

Compensation Chicago, Illinois: ($117,500.00 – $174,350.00) annually San Jose, California: ($129,500.00 – $191,950.00) annually Austin, Texas: ($117,500.00 – $174,350.00) annually Scottsdale, Arizona: ($111,500.00 – $165,550.00) annually Omaha, Nebraska: ($111,500.00 – $165,550.00) annually Additional compensation may include an annual performance bonus, equity, or other incentive compensation. Work Model PayPal’s hybrid work model offers 3 days in the office and 2 days at a location of your choice, ensuring a balance of in‑person collaboration and remote flexibility. Benefits We offer comprehensive, choice‑based programs supporting physical, emotional, and financial wellbeing, including generous paid time off, healthcare coverage for you and your family, and resources to create financial security and mental health support.

Equal Employment Opportunity PayPal provides equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, pregnancy, sexual orientation, gender identity and/or expression, genetic information, marital status, public assistance status, veteran status, or any other characteristic protected by federal, state, or local law. PayPal will provide reasonable accommodations for qualified individuals with disabilities. If you are unable to submit an application because of incompatible assistive technology or a disability, please contact paypalglobaltalentacquisition@paypal.com. #J-18808-Ljbffr