Engineering Manager - Machine Learning Infrastructure

Plaid

🔍 Observed

$241K - $400K USD

San Francisco First seen 3 weeks ago Last seen 4 hours, 5 minutes ago Lever

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Job Description

AI Summary

• Engineering Manager leading a senior ML infrastructure team responsible for building platforms that enable model experimentation, training, deployment, and monitoring at scale across Plaid's financial data network. • Key requirements include 8–10 years in ML infrastructure with hands-on engineering experience, 2+ years managing platform/infrastructure engineers, and proven ability to deliver and operate ML systems at scale. • Primary responsibilities include launching a next-generation feature store, establishing ML Ops best practices, ensuring operational excellence of ML pipelines, and partnering with product teams to accelerate model development. • Must demonstrate strong technical depth across ML infrastructure domains (feature stores, pipelines, deployment, inference, observability) and proven ability to drive execution on complex cross-functional projects. • Role is execution-driven with focus on translating strategy into action, removing blockers, recruiting and mentoring senior engineers, and building a culture of technical excellence and ownership.

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.

Responsibilities: 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. Qualifications: 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.

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Job Information

Company:
Plaid
Location:
San Francisco
Job Type:
Full-Time
Experience Level:
Senior
Source:
Lever
Status:
Active

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65 /100

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