# Staff Engineer

**Company**: PayPal
**Location**: San Jose, California
**Work arrangement**: hybrid
**Experience**: staff
**Job type**: full-time
**Salary**: $196,500.00 - $291,500.00 Annually
**Category**: Engineering
**Industry**: Technology

**Apply**: https://paypal.wd1.myworkdayjobs.com/en-US/jobs/job/San-Jose-California-United-States-of-America/Staff-Machine-Learning-Engineer_R0138553?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_71001d4e-093

## Description

As a Staff Engineer at PayPal, you will lead the design, development, and implementation of advanced machine learning (ML), large language model (LLM), and agentic AI systems that solve complex business and customer problems.

You will translate emerging AI capabilities into scalable, reliable solutions that improve products, strengthen internal operations, and create measurable business value. This role combines hands-on engineering, architectural leadership, and close collaboration with data scientists, software engineers, product managers, and business stakeholders.

**Essential 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.

**Minimum Qualifications:**

- 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.

**Additional Responsibilities & Preferred Qualifications:**

- Master’s or PhD in Computer Science, Machine Learning, or related fields.

- Proven experience designing and developing deep neural network (DNN) solutions.

- Experience building large-scale ads, recommendation, search, and ranking systems, and user modeling (e.g., 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 (e.g., 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 evals and hallucination detection.

- Experience optimizing LLM inference for latency and cost (e.g., vLLM, quantization, distillation, GPU serving).

**Benefits:** At PayPal, we offer comprehensive, choice-based programs to support all aspects of personal wellbeing,physical, emotional, and financial,delivering meaningful value where it matters most. We strive to create a flexible, balanced work culture with a holistic approach to benefits, including generous paid time off, healthcare coverage for you and your family, and resources to create financial security and support your mental health.

**Salary:** The expected range of pay for this role by location is:

- San Jose, California: ($196,500.00 - $291,500.00 Annually)

For more fresh new jobs, check out [yubhub.co](https://yubhub.co/).

## Skills

### Required
- Machine Learning
- TensorFlow
- PyTorch
- scikit-learn
- Cloud Platforms
- AWS
- Azure
- GCP

### Nice to have
- Deep Neural Networks
- Large Language Models
- Agentic AI Systems
- LangGraph
- LangChain
- AutoGen
- Model Context Protocol (MCP)
- LLM Evaluation
- Guardrails
- Observability
- LLM Inference Optimization

---

Source: [Apply at paypal.wd1.myworkdayjobs.com](https://paypal.wd1.myworkdayjobs.com/en-US/jobs/job/San-Jose-California-United-States-of-America/Staff-Machine-Learning-Engineer_R0138553?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)