4 months ago
Senior Software Engineer - ML 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 with ownership of production ML infrastructure including deployment frameworks, inference tooling, and monitoring systems. The role establishes governance through standardized MLOps 'golden path' and operates in a regulated financial context (Plaid processes financial data), with explicit responsibility for reliability and security of ML platforms.
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 a Senior Software Engineer on the Machine Learning Infrastructure team, you will design, build, and operate the systems that power machine learning across Plaid. You will apply your deep technical expertise to create scalable, reliable, and secure ML platforms, and collaborate closely with ML product teams to accelerate the delivery of ML & AI-powered products.
This is a highly technical, hands-on role where you’ll contribute to core infrastructure, influence architectural direction, and mentor peers while helping to define the “golden path” for ML development and deployment at Plaid.