Staff Machine Learning Engineer

PayPal•San Jose, CA

1d•Hybrid

About The Position

As a Staff Machine Learning Engineer on the Personalization team, you will define and drive the technical roadmap for large-scale personalization and real-time decisioning systems at PayPal. You will architect high-performance, low-latency inference services that support model selection, ranking, segmentation, and message optimization across customer-facing surfaces. Your responsibilities include designing and maintaining end-to-end ML workflows—spanning feature pipelines, training, validation, deployment, monitoring, and continual improvement. You will partner closely with data scientists to productionize experimental models and advance experimentation frameworks that enable reliable A/B testing and rapid iteration. You will champion observability, responsible AI, and governance practices through performance monitoring, drift detection, explainability, and compliance. As a technical leader, you will guide design reviews, elevate engineering standards, mentor ML engineers, and influence product and organizational strategy. You will also prototype emerging ML technologies to improve developer efficiency, model quality, and platform scalability, shaping the future of personalization at PayPal.

Requirements

5+ years relevant experience and a Bachelor’s degree OR Any equivalent combination of education and experience.
Extensive experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn.
Expertise in cloud platforms (AWS, Azure, GCP) and tools for data processing and model deployment.

Nice To Haves

Expertise in recommendation systems, ranking models, embeddings, personalization pipelines, or real-time decisioning systems.
Deep experience with distributed systems (Kafka, Flink, Spark) and cloud-native architectures for large-scale data processing and model serving.
Strong proficiency in Python, ML frameworks (e.g., PyTorch, TensorFlow, XGBoost), and modern MLOps tooling (feature stores, model registries, CI/CD, observability).
Proven ability to design highly scalable, low-latency inference services with robust performance, reliability, and monitoring.
Experience leading complex technical initiatives and influencing engineering strategy across teams.
Excellent communication and collaboration skills, especially in distributed team environments.
Experience with real-time inference platforms or feature stores.
Familiarity with model monitoring, drift detection, and MLOps tooling.
Exposure to deep learning frameworks (TensorFlow, PyTorch) or large-scale embeddings.
Background with containerization and orchestration (Docker, Kubernetes).
Experience supporting experimentation pipelines or A/B testing at scale.

Responsibilities

Lead the development and optimization of advanced machine learning models.
Oversee the preprocessing and analysis of large datasets.
Deploy and maintain ML solutions in production environments.
Collaborate with cross-functional teams to integrate ML models into products and services.
Monitor and evaluate the performance of deployed models, making necessary adjustments.