Plaid operates a financial data network and API platform that lets businesses connect to financial institutions and build financial experiences. Its products support account and identity verification, real-time balance and transaction data, investment and liability data, income and underwriting workflows, fraud and AML risk checks, and multi-rail bank payments. It serves developers, businesses, financial institutions, platforms, lenders, banks, and consumer-facing financial-product providers.

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Skills

Candidate Availability

Hybrid · San Francisco · Required

Required and preferred rules are kept separate and reflect the wording in the original posting.

About the Role

Lead a newly formed team focused on scaling AI initiatives and accelerating Plaid's transformation into an AI-first organization. Manage four engineers, define team strategy and execution, partner with technical and product leaders, and scale AI-powered customer and fintech experiences.

Requirements

8+ years of industry experience, including Staff-level engineering experience before management.
1+ year of engineering management experience.
Hands-on experience building and shipping LLM-powered products using prompt engineering, fine-tuning, RAG, semantic search, vector databases, embedding models, agent orchestration, evaluation, monitoring, streaming, and SSE.
Knowledge of UX and design patterns for GenAI products.
Experience with user research, rapid experimentation, and understanding customer needs.
Track record of building and growing high-performing engineering teams.
Ability to balance divergent and convergent thinking for zero-to-one projects.
Curiosity and passion for GenAI applications.
Preferred: experience training or serving ML models in production and working in privacy- or PII-sensitive environments.

Responsibilities

Lead and develop a team of four engineers through goal setting, coaching, and feedback.
Define long-term team strategy and manage execution with technical and product leaders.
Lead projects for major AI customers and partners, beginning with personal finance use cases.
Develop integration patterns with AI providers, including official MCP Servers.
Improve conversational interfaces, agentic commerce trust infrastructure, and AI-powered customer support.
Scale multi-turn and multi-agent systems, offline evaluation, reinforcement learning, and customer-specific memory.
Extend agentic systems across product recommendation, onboarding, risk diligence, activation, and upselling.

Benefits

Medical insurance
Dental insurance
Vision insurance
401(k)
Equity