Senior Staff Machine Learning Engineer

Job Summary: Job Description: PayPal, Inc. seeks Senior Staff Machine Learning Engineer in San Jose, CA Job

Duties: Define and drive the strategic vision for implementing machine learning (ML) functions into the software ecosystem. Analyze software product architecture to create ML models using algorithmic programming techniques, database management practices, data visualization methods, and related query languages. Collaborate with Engineering and Data Science teams throughout the design and development phases to create new and enhanced software products and features consistent with business and technical requirements, with a focus on functionality, performance, scalability, reliability, realistic implementation schedules, and adherence to development goals and principles.

Lead the optimization of ML models to integrate them into products and services. Monitor and evaluate the performance of deployed models, making necessary adjustments. Organize and analyze large datasets using experience with cloud platforms and tools for data processing and model deployment.

Create and implement data analytics pipelines into existing software. Deploy and maintain ML solutions in production environments. Define and design testing sequences for newly developed software to implement into the ML pipeline.

Create automated tests and deliver high-quality software code to production within a short development cycle in the continuous integration and delivery environment. Partial telecommuting permitted from within a commutable distance. Minimum

Requirements: Bachelor's degree, or foreign equivalent, in Computer Science, Data Science, Information Systems, or a closely related field plus 8 years of progressively responsible experience in the job offered or a related occupation. Special Skill

Requirements: (1) Experience developing ML models end-to-end (2) Experience with managing external facing ML models (3) Experience in ML tooling (4) Experience assessing predictive value of features (5) Experience with Python and SQL programming languages (8 years) (6) Experience with Credit and Fraud Risk models (7) Experience in distributed computing technologies like Spark (8) Banking and fintech experience and experience with Model Risk Management requirements (9) AWS, Azure, or GCP (8 years) (10) Tools for data processing and model deployment (8 years) (11) Experience leading the design, implementation, and deployment of machine learning models (6 years) (12) Continuous Integration and Continuous Delivery (CI/CD) Pipelines (6 years) (13) PyTorch, TensorFlow, XGBoost, and Scikit-learn Machine Learning Libraries (6 years) (14) AWS SageMaker (6 years) (15) Natural Language Processing (8 years) (16) Statistical Models (8 years) (17) Agile Methodology (5 years) (18) Artificial Neural Networks (8 years) Additional Responsibilities & Preferred

Qualifications: EOE, including disability/vets. The base pay for this role will depend on where you work and the relevant experience and expertise you bring. The expected range of pay for this role by location is: Primary Location | Pay Range: San Jose, California | Salary: $227,639.00-300,500.00 per annum.

40 hours per week; M-F, 9:00 a.m. to 5:00 p.m. Additional compensation for this role may include an annual performance bonus, equity, or other incentive compensation, as applicable. Must be legally authorized to work in the U.S. without sponsorship.

Subsidiary: PayPal Travel Percent: 0 PayPal does not charge candidates any fees for courses, applications, resume reviews, interviews, background checks, or onboarding. When making an application directly, we will never ask you to share passwords, one-time passcodes (OTP), or verification codes. Any such request is a red flag and likely part of a scam.

All communication regarding your application will come from official PayPal email domains. If you suspect fraudulent activity, please report it immediately. To learn more about how to identify and avoid recruitment fraud please visit https://careers.pypl.com/contact-us.

For the majority of employees, PayPal's balanced hybrid work model offers 3 days in the office for effective in-person collaboration and 2 days at your choice of either the PayPal office or your home workspace, ensuring that you equally have the benefits and conveniences of both locations. Our