Senior ML Engineer, ML Infra & Production

Plaid; New York (NY); On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

Equity included

Commission opportunities

Medical, dental, vision benefits

401(k)

Job summary

Plaid in New York is seeking a Senior Machine Learning Engineer to own high-performance feature computation and online inference pipelines powering production ML systems. You will build observability, monitoring, and debugging capabilities while collaborating with ML Infrastructure, Data Science, and Product teams to deliver scalable ML solutions.

You should have 6+ years in production ML, strong ML infrastructure experience, and proficiency with Python and PyTorch, Spark, SageMaker, and

Qualifications

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

Responsibilities

• Build and scale machine learning systems powering a fast-growing fraud-detection product.
• Tackle complex challenges at the intersection of ML, data infrastructure, and production reliability.
• Develop foundational ML capabilities using Plaid’s financial network data to detect and prevent fraud.
• Collaborate with engineers, data scientists, and cross-functional partners to deliver high-impact ML solutions.

Skills

Python

ML infrastructure

Production ML

End-to-end ML

Observability

Tools

PyTorch

Spark

SageMaker

Airflow

Job description

Plaid in New York is seeking a Senior Machine Learning Engineer to own high-performance feature computation and online inference pipelines powering production ML systems. You will build observability, monitoring, and debugging capabilities while collaborating with ML Infrastructure, Data Science, and Product teams to deliver scalable ML solutions.

You should have 6+ years in production ML, strong ML infrastructure experience, and proficiency with Python and PyTorch, Spark, SageMaker, and