Machine Learning Engineer PhD Intern

As a Machine Learning Engineer PhD Intern, you will work with experienced engineers, data scientists, and product teams to research, develop, and evaluate machine learning solutions.

You’ll take on complex technical problems, work with large datasets, and contribute to model development and deployment.

Your responsibilities will include:

• Conducting applied research to identify and develop machine learning solutions for complex business challenges.
• Designing, building, and evaluating machine learning models, using appropriate methods to assess their effectiveness, reliability, and suitability for use.
• Analyzing data and conducting experiments to generate insights and guide model development.
• Collaborating with engineers, data scientists, and cross-functional partners to translate business needs into scalable machine learning solutions.
• Contributing to the development and deployment of machine learning pipelines and solutions in production environments.
• Monitoring model performance and identifying opportunities to improve outcomes, reliability, and efficiency.
• Documenting research, experiments, and technical decisions, and communicating findings to technical and non-technical audiences.
• Staying informed about developments in machine learning and assessing their potential relevance to PayPal’s products and services.

To qualify, you must be currently pursuing a PhD in computer science, machine learning, statistics, or a related field, with a strong foundation in machine learning and statistics, and proficiency in Python and experience with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn.

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: San Jose, California | ($59- $66 hourly).

PayPal offers comprehensive, choice-based programs to support all aspects of personal wellbeing,physical, emotional, and financial,delivering meaningful value where it matters most.