Senior ML Engineer — Fraud Infra & Production ML

Plaid; San Francisco (CA); On-site; USD 229,000 - 315,000; Full time

Job summary

Plaid’s Data team within the Fraud organization builds the machine learning systems that power Plaid’s fraud detection products, leveraging Plaid’s unique network data to identify and stop fraud before it happens. As a Senior Machine Learning Engineer, you will own the development of high-performance feature computation and online inference pipelines that power production machine learning systems at scale.

You’ll build robust observability, monitoring, and automated debugging capabilities, while

Qualifications

• 6+ years of relevant experience, with a strong focus on building, deploying, and scaling production machine learning systems.
• Strong experience with ML infrastructure and operations, including production 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

Python

Machine Learning

ML infrastructure

PyTorch

Spark

SageMaker

Airflow

Tools

PyTorch

Spark

SageMaker

Airflow

Job description

Plaid’s Data team within the Fraud organization builds the machine learning systems that power Plaid’s fraud detection products, leveraging Plaid’s unique network data to identify and stop fraud before it happens. As a Senior Machine Learning Engineer, you will own the development of high-performance feature computation and online inference pipelines that power production machine learning systems at scale.

You’ll build robust observability, monitoring, and automated debugging capabilities, while