Staff Machine Learning Engineer at PayPal

San Jose, California, United States -

Full Time

Start Date

Immediate

Expiry Date

27 Mar, 26

Salary

0.0

Posted On

27 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Python, TensorFlow, PyTorch, Scikit-learn, AWS, Azure, GCP, Recommendation Systems, Distributed Systems, MLOps, Docker, Kubernetes, Data Processing, Model Deployment, Real-time Decisioning

Industry

Software Development

Description

Your responsibilities include designing and maintaining end-to-end ML workflows—spanning feature pipelines, training, validation, deployment, monitoring, and continual improvement. 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. 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. 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

Design and maintain end-to-end ML workflows, including feature pipelines, training, validation, deployment, and monitoring. Collaborate with cross-functional teams to integrate ML models into products and services.