Senior ML Engineer – Production Infra & Fraud ML

Plaid; Seattle (WA); On-site; USD 229,000 - 315,000; Full time

Job summary

Plaid is seeking a Senior Machine Learning Engineer in Seattle to own high-performance feature computation and online inference pipelines in production ML systems. You will build observability, monitoring, and automated debugging capabilities to ensure reliability at scale.

You will partner with ML Infrastructure, Data Science, and Product teams to deliver scalable ML solutions that power fraud detection and protect customers across Plaid’s network.

Qualifications

• 6+ years of experience building, deploying, and scaling production machine learning systems.
• Strong experience with ML infrastructure, deployment, monitoring, and reliability.
• Proven ability to independently own and deliver complex, end-to-end machine learning engineering projects.
• Proficiency with Python and experience with ML and data technologies such as PyTorch, Spark, SageMaker, and Airflow.

Responsibilities

• Build and scale machine learning systems that power a rapidly growing fraud detection product in a fast-paced environment.
• Solve complex technical challenges at the intersection of machine learning, data infrastructure, and production reliability.
• Develop foundational ML capabilities that leverage Plaid’s extensive financial network data to detect and prevent fraud.
• Collaborate closely with engineers, data scientists, and cross-functional partners across Plaid to deliver high-impact solutions.

Skills

ML infrastructure

Production deployment

Python

PyTorch

Spark

SageMaker

Airflow

Job description

Plaid is seeking a Senior Machine Learning Engineer in Seattle to own high-performance feature computation and online inference pipelines in production ML systems. You will build observability, monitoring, and automated debugging capabilities to ensure reliability at scale.

You will partner with ML Infrastructure, Data Science, and Product teams to deliver scalable ML solutions that power fraud detection and protect customers across Plaid’s network.