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You’ll be a machine learning engineer on the Data Foundation & AI team. In this role, you will design, build, and scale advanced ML/AI systems that power Plaid products and applications. Your work may span developing reliable distributed training and serving systems, improving ML operations at scale, and building AI-powered applications that enable new product experiences.
Job Responsibility:
Building and scaling advanced ML/AI systems that power core Plaid products and applications used by millions of consumers
Driving impact at scale by improving distributed training, serving, and ML operations to make Plaid’s AI capabilities faster, more reliable, and more widely available
Developing new AI applications that enable innovative product experiences across fintech
Tackling 0 to 1 problems where you explore new approaches, as well as scaling 1 to 10 systems for reliability and efficiency
Collaborating with some of the strongest MLEs at Plaid in a high-ownership, bottom-up driven team
Experimenting with cutting-edge ML and AI techniques while balancing practical productionization and measurable business impact
Requirements:
1-3 years of experience training, deploying, and scaling ML/AI models in production environments
Strong experience with distributed systems and ML operations — from large-scale training to low-latency serving and monitoring
Proficiency in Python and modern ML frameworks (e.g., PyTorch), with the ability to implement and optimize complex models
Hands-on experience building or scaling ML/AI infrastructure, pipelines, or reusable platforms that support multiple teams
Curiosity and drive to experiment with advanced AI techniques (e.g., embeddings, retrieval, generative modeling) while staying grounded in production impact
Ability to thrive in a collaborative environment, working with both technical and non-technical partners to drive measurable outcomes
Nice to have:
Experience applying ML/AI in fintech or similarly regulated industries is a plus
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