Engineering Manager- Machine Learning Infrastructure

Plaid is evolving into an AI-first company, where data and machine learning are the key enablers of smarter, more secure insight products built on top of Plaid’s vast financial data network. The Machine Learning Infrastructure team sits at the center of this transformation. We build the platforms that enable model developers to experiment, train, deploy, and monitor machine learning systems reliably and at scale — from feature stores and pipelines, to deployment frameworks and inference tooling. We are in the midst of a pivotal shift: replacing legacy systems with a modern feature store, and establishing a standardized ML Ops “golden path.” Our mission is to enable Plaid’s product teams to move faster with trustworthy insights, deploy models with confidence, and unlock the next generation of AI-powered financial experiences.

As the Engineering Manager for Machine Learning Infrastructure, you will be responsible for guiding a senior engineering team through the design, delivery, and operation of Plaid’s ML infrastructure. We are looking for a leader who combines deep technical expertise in ML infrastructure with proven experience scaling and managing senior engineering teams. You’ll ensure clarity of execution, help your team deliver high-quality systems, and partner closely with ML product teams to meet their needs. This role is execution-driven: you will translate strategy into action, remove blockers, and build a culture of ownership and technical excellence.

• Lead and support the ML Infra team, driving project execution and ensuring delivery on key commitments.
• Build and launch Plaid’s next-generation feature store to improve reliability and velocity of model development.
• Define and drive adoption of an ML Ops “golden path” for secure, scalable model training, deployment, and monitoring.
• Ensure operational excellence of ML pipelines, deployment tooling, and inference systems.
• Partner with ML product teams to understand requirements and deliver solutions that accelerate model development and iteration.
• Recruit, mentor, and develop engineers, fostering a collaborative and high-performing team culture.

• 8–10 years of experience in ML infrastructure, including direct hands-on expertise as an engineer, IC/TL.
• 2+ years of experience managing infrastructure or ML platform engineers.
• Proven experience delivering and operating ML or AI infrastructure at scale.
• Solid technical depth across ML/AI infrastructure domains (e.g., feature stores, pipelines, deployment, inference, observability).
• Demonstrated ability to drive execution on complex technical projects with cross-team stakeholders.
• Strong communication and stakeholder management skills.