4 months ago
Engineering Manager - Machine Learning Infrastructure
Plaid
San Francisco
Full-time
GenOps Responsibility Profile
Runtime Ownership
✓ Yes
Control Plane
✓ Yes
Governance / Policy
✓ Yes
Observability / Telemetry
✓ Yes
Incident / Reliability
✓ Yes
Regulated Context
✓ Yes
AI / Model Runtime
✓ Yes
Primary Domain
ai platform
Production Environment
prod
Classification Confidence
95%
Rationale: This is a clear GenOps role managing ML infrastructure platforms that enable model deployment, monitoring, and inference at scale in a regulated financial context. The role owns production ML systems including feature stores, deployment frameworks, and inference tooling, with explicit responsibility for reliability, standardization (MLOps golden path), and enabling trustworthy AI-powered products.
Job Description
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.