Senior Staff Machine Learning Engineer

PayPal•San Jose, CA

6d•$227,639 - $300,500•Hybrid

About The Position

PayPal, Inc. seeks Senior Staff Machine Learning Engineer in San Jose, CA. This role involves defining and driving the strategic vision for implementing machine learning (ML) functions into the software ecosystem. The engineer will analyze software product architecture to create ML models using algorithmic programming techniques, database management practices, data visualization methods, and related query languages. Collaboration with Engineering and Data Science teams is key throughout the design and development phases to create new and enhanced software products and features consistent with business and technical requirements, focusing on functionality, performance, scalability, reliability, realistic implementation schedules, and adherence to development goals and principles. The role includes leading the optimization of ML models for integration into products and services, monitoring and evaluating the performance of deployed models, and making necessary adjustments. Organizing and analyzing large datasets using cloud platforms and tools for data processing and model deployment, creating and implementing data analytics pipelines into existing software, and deploying and maintaining ML solutions in production environments are also core duties. Additionally, the engineer will 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 is permitted from within a commutable distance.

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.
Experience developing ML models end-to-end
Experience with managing external facing ML models
Experience in ML tooling
Experience assessing predictive value of features
Experience with Python and SQL programming languages (8 years)
Experience with Credit and Fraud Risk models
Experience in distributed computing technologies like Spark
Banking and fintech experience and experience with Model Risk Management requirements
AWS, Azure, or GCP (8 years)
Tools for data processing and model deployment (8 years)
Experience leading the design, implementation, and deployment of machine learning models (6 years)
Continuous Integration and Continuous Delivery (CI/CD) Pipelines (6 years)
PyTorch, TensorFlow, XGBoost, and Scikit-learn Machine Learning Libraries (6 years)
AWS SageMaker (6 years)
Natural Language Processing (8 years)
Statistical Models (8 years)
Artificial Neural Networks (8 years)

Nice To Haves

Agile Methodology (5 years)

Responsibilities

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.
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.