Staff Machine Learning Engineer

PayPal

Confirmed live today High trust

Hybrid

Quick summary

Work type: Hybrid
Location: San Jose, CA
Salary: $227,639–$300,500 / yr
Posted: 3 days ago
Freshness: Confirmed live today
Closes: Mar 10, 2027
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $227k

This role $264k

$171k most similar roles pay here $314k

This role pays more than 80% of similar roles. Most pay $197,981–$256,950 — the shaded band above. At the midpoint, this role pays about $264k versus about $227k for comparable roles.

Based on 240 similar postings.

Employer

About PayPal

PayPal is a leading global digital wallet and online payment system, founded in 1998, that allows individuals and businesses to send, receive, and manage funds securely in over 200 markets.

PayPal currently has 74 open roles on FindRole.

Listed pay typically runs $160,500–$235,826 across 74 roles with salary data.

Most-posted roles

View all roles at PayPal

At a glance

TL;DR · Staff Machine Learning Engineer

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As a Staff Machine Learning Engineer, you will join a team dedicated to designing and deploying scalable risk analytical solutions and generative AI systems to enhance customer experience. You will productize machine learning models, lead cross-functional teams of data scientists and system engineers, and develop AI agents to automate workflows and detect live issues. Your daily work involves defining technical standards, ensuring high code quality through rigorous testing, and researching state-of-the-art techniques. The role requires proficiency in Python, Java, and big data frameworks like Spark, Hadoop, or Flink. You will utilize tools such as LangChain, LlamaIndex, Airflow, and MLflow while managing workloads on cloud platforms with Docker and Kubernetes. This position focuses on solving complex problems in risk analytics, fraud detection, anomaly detection, payment risk, and transaction monitoring within a large-scale production environment.

What does a Machine Learning Engineer earn in California?

Median $246150 from 178 postings across 30 companies.

See salary data

Skills

Generative AI LLMs RAG LangChain LlamaIndex Python Java Spark Hadoop Flink AWS GCP Azure Docker Kubernetes Airflow MLflow CI/CD Git Object-Oriented Design

What you'll do

Design and deploy scalable risk analytical solutions and generative AI models to enhance customer experience.
Productize machine learning models and develop AI agents to automate workflows and detect live issues.
Lead architecture design and technical decision-making across cross-functional teams in a matrix organization.
Build and maintain data pipelines and ML feature engineering systems using MLOps platforms.
Research and integrate state-of-the-art machine learning techniques, tools, and frameworks into the platform.
Ensure high code quality and reliability through rigorous testing, code reviews, and software development best practices.
Mentor junior and senior team members while providing technical guidance and strategic direction.

What we're looking for

A Master's degree in Computer Science or Engineering with five years of experience is required, or a Bachelor's degree with eight years of experience.
Experience designing and deploying scalable machine learning models and pipelines in production environments using Python and Java (4 years).
Experience developing and productizing Generative AI solutions, including LLMs, RAG pipelines, or AI agents (2 years).
Experience with big data technologies and distributed computing frameworks such as Spark, Hadoop, or Flink (3 years).
Experience designing and developing large-scale software applications using Object-Oriented Design in Java or Python (4 years).
Experience with cloud platforms (AWS, GCP, or Azure) for managing ML/AI workloads including Docker and Kubernetes (2 years).
Experience building data pipelines and MLOps systems using tools like Airflow or MLflow (2 years).
Experience in risk analytics or fraud detection domains, including model development for anomaly detection or payment risk (2 years).
Experience developing AI agents or workflow automation systems for issue detection and automated remediation (1 year).
Experience with software engineering best practices including code reviews, testing, CI/CD pipelines, and Git (4 years).
Experience leading cross-functional teams of data scientists and engineers to deliver end-to-end ML/AI solutions in a matrix organization (3 years).