Staff Machine Learning Engineer

In this Staff Machine Learning Engineer role, you will help shape advanced machine learning and AI capabilities designed to address complex business and customer challenges. Based in San Jose, CA (hybrid), the work blends hands-on model building with architectural leadership, alongside close collaboration across data science, software engineering, product, and business stakeholders.

You will lead end-to-end efforts that span dataset analysis, production deployment, and ongoing performance evaluation, including systems built for large language models and agentic AI. The focus is on delivering reliable ML experiences through strong engineering practices, evaluation, and operational excellence.

What you’ll do

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

What you bring

• 5+ years of relevant experience and a Bachelor’s degree, or an equivalent combination of education and experience.
• Extensive experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Expertise with AWS, Azure, and GCP, plus tools for data processing and model deployment.
• Master’s or PhD in Computer Science, Machine Learning, or a related field.
• Proven experience designing and developing deep neural network (DNN) solutions.
• Experience building large-scale ads, recommendation, search and ranking, and user modeling (including personalization, embeddings, retrieval, learning-to-rank, and user behavior and intent modeling).
• Hands-on experience building and shipping LLM applications in production, including fine-tuning (LoRA/PEFT), retrieval-augmented generation (RAG), and prompt engineering.
• Experience designing agentic systems with tool use, planning, memory, and multi-agent orchestration, using frameworks such as LangGraph, LangChain, AutoGen, or Model Context Protocol (MCP).
• Experience with LLM and agent evaluation, guardrails, and observability, including offline/online evaluation and hallucination detection.
• Experience optimizing LLM inference for latency and cost, including vLLM, quantization, distillation, and GPU serving.

Key technologies

• TensorFlow, PyTorch, scikit-learn
• AWS, Azure, GCP
• LoRA/PEFT, RAG
• LangGraph, LangChain, AutoGen, Model Context Protocol (MCP)
• vLLM, quantization, distillation, GPU serving
• Deep neural network (DNN)

Compensation and work location

• Location: San Jose, California (hybrid)
• Pay range: USD 196,500 - 291,500 per year
• Additional compensation: An annual performance bonus, equity, or other incentive compensation may apply.
• Travel: 0%

Benefits

• Generous paid time off
• Healthcare coverage for you and your family
• Resources to create financial security
• Support for your mental health

For the majority of employees, PayPal’s balanced hybrid work model provides 3 days in the office for in-person collaboration and 2 days at your choice of either the PayPal office or your home workspace.