Staff Machine Learning Engineer

Job Summary:
This job will lead the design, development, and implementation of advanced machine learning models and algorithms to solve complex problems. You will work closely with data scientists, software engineers, and product teams to enhance services through innovative AI/ML solutions. Your role will involve building scalable ML pipelines, ensuring data quality, and deploying models into production environments to drive business insights and improve customer experiences.

Job Description:

PayPal, Inc. seeks Staff Machine Learning Engineer in San Jose, CA

Job Duties: Manage FSI Data Integration Project with Risk Models. Work with Infra and Online Engineering Teams to ensure the relevant features can and will be exposed to the different system checkpoints. Verify and validate exposure as online variables, such as existence and quality. Work with the Global Analytics and Data Science teams to enumerate the features. Perform feature comparisons to identify where the unique features engineered by FSI can provide measurable uplift. This work will scale across models for multiple fraud domains, including account takeover, collusion, seller risk, credit risk, and more. Work with model developers on a development plan and will ensure they are able to use FSI features and assist them in case of issues. Create a New Risk Model Based on Linking Features. Work with a Data Science team that developed LEGO, an internal graph-based tool for generating online variables, to check if it could be used for FSI objectives. Investigate embedding features to be used as a possible advanced solution for upstream tasks that would be able to represent the entire FSI intelligence in just a few features. Develop a proof of concept (POC) and will perform a results comparison with the current solution to ensure better outcomes. Develop the complete automated tool and will perform the required experiments to ensure it works as expected and monitors its performance. Transform FSI Data into Graph Infrastructure. Apply Graph Data Science Insights to FSI Linking Automation. Automate the Detection of New Risky Features. Telecommuting permitted - work may be performed from anywhere in the US.

Additional Responsibilities & Preferred Qualifications:

Minimum Requirements: Master's degree, or foreign equivalent, in Computer Science, Engineering (any field), or a closely related field plus four years of experience in the job offered or a related occupation. Employer will accept a Bachelor's degree, or foreign equivalent, in Computer Science, Engineering (any field) or a closely related field plus six years of experience in the job offered or a related occupation.

Special Skill Requirements:

Experience in the financial fraud detection domain, including developing machine learning models for identifying fraudulent activity. (4 years)

Experience translating business stakeholder requirements into machine learning problem formulations, including defining key performance indicators (KPIs), evaluation metrics, and success criteria for model development projects. (4 years)

Experience in end-to-end design and development of machine learning solutions, from data collection, feature engineering, preprocessing, feature selection, research, model architecture, training, optimization, evaluation, model encoding, deployment to production, and maintenance. (4 years)

Experience automating end-to-end machine learning solutions, including automated data collection, model training, model encoding, and deployment to production environments. (4 years)

Experience with Python-based data science libraries, including scikit-learn, pandas, NumPy, and Keras, for preprocessing, training, and evaluating machine learning models. (4 years)

Experience working with large-scale tabular datasets in cloud-based data warehouse environments for feature engineering and analytics. (4 years)

Experience encoding and optimizing machine learning models for production deployment using serialization frameworks such as ONNX for cross-platform model inference. (4 years)

Experience in embedding representation methods for tabular data. (1 year)

Experience collaborating with cross-functional teams, including business strategy, engineering, data infrastructure, and data science teams to align machine learning model outputs with business objectives and integrate models into production decision systems. (4 years)

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, CA | Salary: $196,833.00-333,500.00 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 Benefits:

At PayPal, we're committed to building an equitable and inclusive global economy. And we can't do this without our most important asset-you. That's why we offer comprehensive, choice-based programs, to support all aspects of personal wellbeing-physical, emotional, and financial-delivering meaningful value where it matters most. We strive to create a flexible, balanced work culture with a holistic approach to benefits, including generous paid time off, healthcare coverage for you and your family, and resources to create financial security and support your mental health.

Any general requests for consideration of your skills, please Join our Talent Community.

We know the confidence gap and imposter syndrome can get in the way of meeting spectacular candidates. Please don't hesitate to apply.