Senior Software Engineer - ML 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 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. Responsibilities

Design and implement large-scale ML infrastructure, including feature stores, pipelines, deployment tooling, and inference systems.

Drive the rollout of Plaid’s next-generation feature store to improve reliability and velocity of model development.

Help define and evangelize an ML Ops “golden path” for secure, scalable model training, deployment, and monitoring.

Ensure operational excellence of ML pipelines and services, including reliability, scalability, performance, and cost efficiency.

Collaborate with ML product teams to understand requirements and deliver solutions that accelerate experimentation and iteration.

Contribute to technical strategy and architecture discussions within the team.

Mentor and support other engineers through code reviews, design discussions, and technical guidance.

Qualifications

5+ years of industry experience as a software engineer, with strong focus on ML/AI infrastructure or large-scale distributed systems.

Hands-on expertise in building and operating ML platforms (e.g., feature stores, data pipelines, training/inference frameworks).

Proven experience delivering reliable and scalable infrastructure in production.

Solid understanding of ML Ops concepts and tooling, as well as best practices for observability, security, and reliability.

Strong communication skills and ability to collaborate across teams.

[Nice to have] Experience with ML Ops tools such as MLFlow, SageMaker, or model registries.

[Nice to have] Exposure to modern AI infrastructure environments (LLMs, real-time inference, agentic models).

[Nice to have] Background in scaling ML infrastructure in fast-paced product environments.