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Plaid is seeking a Senior Machine Learning Engineer to join the Data team within the Fraud organization. The role focuses on building and scaling machine learning systems that power Plaid's fraud detection products, leveraging Plaid's unique network data across 12,000 financial institutions in the US, Canada, UK, and Europe.
You will own the development of high-performance feature computation and online inference pipelines that power production machine learning systems at scale. Key responsibilities include building robust observability, monitoring, and automated debugging capabilities while leveraging AI-assisted tools to investigate complex system behavior and maintain high reliability. You'll partner closely with ML Infrastructure, Data Science, and Product teams to execute critical technical initiatives and deliver scalable, high-impact ML solutions.
The role involves solving complex technical challenges at the intersection of machine learning, data infrastructure, and production reliability. You'll develop foundational ML capabilities that detect and prevent fraud, collaborating with engineers, data scientists, and cross-functional partners across Plaid to deliver high-impact solutions.
Plaid powers tools millions of people rely on to manage their finances, working with thousands of companies including Venmo, SoFi, Fortune 500 companies, and major banks. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam.
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
NICE-TO-HAVE:
- Experience in fraud or risk domains
- Experience in Graph machine learning
