Plaid is transforming into an AI-first company, requiring robust machine learning infrastructure to enable smarter, more secure insight products. The Machine Learning Infrastructure team needs to build and operate platforms that support the entire ML lifecycle, from experimentation to deployment and monitoring, to accelerate the delivery of AI-powered financial experiences.
Requirements
- 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.
- Experience with ML Ops tools such as MLFlow, SageMaker, or model registries.
- Exposure to modern AI infrastructure environments (LLMs, real-time inference, agentic models).
- Background in scaling ML infrastructure in fast-paced product environments.
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.
Other
- Strong communication skills and ability to collaborate across teams.
