Sr Machine Learning Engineer

PayPal•Austin, TX

8d•$117,500 - $199,500•Hybrid

About The Position

PayPal, Inc. seeks Sr Machine Learning Engineer in Austin, TX. This role involves evaluating and validating high-impact statistical and AI/ML models across key business areas, performing comprehensive quantitative and qualitative model validation in alignment with internal Model Risk Management (MRM) Policy and industry standards. The engineer will assess model data integrity, design soundness, and performance robustness to identify and report potential risks and deficiencies. Responsibilities include providing risk oversight on the engineering, automation, and production deployment of sophisticated machine learning models. The role requires building scalable systems with Python and cloud platforms, and architecting end-to-end pipelines for real-time data ingestion, model serving, and monitoring. Additionally, the engineer will lead technical projects, coordinate project roadmaps, and mentor junior engineers while integrating cutting-edge methods in deep learning and generative AI. Ensuring system reliability, regulatory compliance, and technical innovation through collaboration with DevOps, IT, and business partners is crucial. The role drives transformative change across products and services through continuous improvement, advanced model tuning, and implementation of system-wide best practices for performance, scalability, and ethical AI use. Partial telecommuting is permitted from within a commutable distance.

Requirements

Master’s degree, or foreign equivalent, in Data Science, Computer Science, Business Analytics, or a closely related field plus two years of experience in the job offered or a related occupation.
Experience designing and implementing scalable machine learning models using algorithms such as logistic regression, random forests, gradient boosting, and time series models to solve business problems (2 years).
Experience utilizing deep learning and Generative AI architectures, including CNNs, RNNs, and large language models (LLMs), to enhance automation and decision-making (2 years).
Experience conducting exploratory data analysis (EDA), statistical modeling, and visualization to identify trends, detect anomalies, and inform model improvements (2 years).
Experience programming in Python for model development, validation, and automation using libraries such as pandas, scikit-learn, TensorFlow, and PyTorch (2 years).
Experience writing and optimizing complex SQL queries for data extraction, transformation, and analytics in large-scale environments (2 years).
Experience developing, validating, and maintaining statistical and AI/ML models for risk management, fraud detection, AML, and compliance in alignment with Model Risk Management (MRM) standards (2 years).
Experience performing comprehensive model validation, including data quality assessment, benchmarking, back-testing, and performance evaluation (2 years).
Experience preparing model documentation, validation reports, and findings in compliance with internal governance and regulatory standards (2 years).
Must be legally authorized to work in the U.S. without sponsorship.

Responsibilities

Evaluate and validate high-impact statistical and AI/ML models across key business areas.
Perform comprehensive quantitative and qualitative model validation in alignment with internal Model Risk Management (MRM) Policy and industry standards.
Assess model data integrity, design soundness, and performance robustness to identify and report potential risks and deficiencies.
Provide risk oversight on the engineering, automation, and production deployment of sophisticated machine learning models.
Build scalable systems with Python and cloud platforms, and architect end-to-end pipelines for real-time data ingestion, model serving, and monitoring.
Lead technical projects, coordinate project roadmaps, and mentor junior engineers.
Integrate cutting-edge methods in deep learning and generative AI.
Ensure system reliability, regulatory compliance, and technical innovation through collaboration with DevOps, IT, and business partners.
Drive transformative change across products and services through continuous improvement, advanced model tuning, and implementation of system-wide best practices for performance, scalability, and ethical AI use.