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The Data team within Plaid’s Fraud organization builds the machine learning systems that power Plaid’s cutting-edge fraud detection products. By leveraging Plaid’s extensive network data, we enable proactive fraud prevention—stopping fraud before it happens. Our team owns the entire ML lifecycle, from developing feature pipelines and training models to deploying and monitoring them in production. We ensure that our systems scale reliably and efficiently as Plaid continues to grow and support hundreds of customers. As a Staff Machine Learning Engineer on Plaid’s Fraud Data team, you will design and build scalable ML infrastructure that powers our industry-leading fraud detection product. You’ll lead the evolution of our model deployment, monitoring, and observability frameworks to ensure high reliability and performance at scale. Collaborating closely with teams across ML Infrastructure, Product, and Engineering, you’ll deliver robust systems that protect users and customers from fraud. In addition, you’ll mentor other engineers and help shape the long-term technical vision and strategy of the Fraud Data team.
Job Responsibility:
Design and build scalable ML infrastructure for Plaid’s fraud detection product
Working at a fast-pace environment to build a rapidly growing product with a championship team
Solving complex problems at the intersection of ML systems, data, and reliability
Building the foundations for fraud detection on the largest financial dataset in the world
Collaborating with talented engineers and data scientists across Plaid
Requirements:
8+ years total experience
at least 5 years building and deploying production ML systems
proven experience in machine learning infrastructure/operations
demonstrated technical leadership and architectural vision, driving systems from concept to production
proficiency in Python, PyTorch, Spark, SageMaker, and Airflow, or equivalent technologies
Nice to have:
experience working in fraud detection, risk modeling, or financial security domains
background in graph machine learning or related techniques
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