Staff Software Engineer - AI

Plaid•San Francisco, CA

About The Position

We believe that the way people interact with their finances will drastically improve in the next few years. Weâre dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaidâs network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam. AI and intelligent systems are driving the fifth paradigm shift, following previous technological revolutions like mainframes, personal computers, the internet, and mobile devices. We believe, in the foreseeable future, AI will revolutionize the FinTech industry - from how consumers understand and manage their finances, to how developers build applications and how all companies operate. The fintech industry landscape will undergo a fundamental reshape. Plaid in the FinTech AI Ecosystem Plaid is uniquely positioned to become the financial data and insights backbone for AI applications and platforms in this evolving ecosystem. We believe consumers should be able to understand and manage their financial life through conversational AI interfaces using natural language. We believe consumers should have peace of mind with a trustworthy consent and authorization manager when agents shop for them. We believe identity verification and financial fraud prevention in AI-powered products should feel seamless and embedded for the end users. The list goes on. The most important AI companies, major fintechs, and customer agent platforms are actively trying to integrate Plaid into AI-powered products and solutions they build. Plaidâs AI-powered Experiences for Customers The explosive growth in model intelligence and increasing relevance for enterprise tasks offer a unique opportunity for Plaid to rethink from the ground up how we operate and interact with our customers for the most optimal experience. Our early experiments in building AI agents for customer experience are paying off with huge business value. For example, our homegrown AI-powered customer support agents resolve a significant proportion of customer questions and issues within seconds, which would otherwise have required humans hours to work on. This is just the beginning and there is so much more we could do. The AI Applications Team You will have the opportunity to join as one of the founding members of this newly formed team that is dedicated to consolidating and rapidly scaling our successful bets so far, and grow with the team in our quest to accelerate Plaidâs transformation into an AI-first company. In this role you will lead projects that enable and scale our business with our largest AI customers and partners, starting with personal finance use cases and expanding into many others; examples include: - Develop and evolve the preferred integration pattern for Plaid with AI providers - from API adaptations to building the official Plaid MCP Servers, and beyond - Redefine how Plaidâs consumer link experience embed into conversational interfaces in the most seamless way Architect the trust layer for the future of agentic commerce that will become the industry standard Additionally you will be expected to scale and extend our existing successful bets on AI-powered customer experience; examples include: - Make the next step-function improvement in our homegrown customer support agent - land our multi-turn and multi-agent system that powers a truly delightful experience for our customers; define how to scalably run offline evaluation for complex multi-turn open-ended tasks; research and prototype how Human-In-The-Loop - Reinforcement Learning (RLHF) can power an insights flywheel; pioneer the architecture for customer-specific long-term memory, etc. - Extend our agentic system to support other critical parts of the customer journey, starting with areas with the highest ROI - top-of-funnel product recommendation, customer onboarding and risk diligence, customer activation and assistance for faster productionization, as well as upselling and cross-selling of Plaid products You will have a front row seat to all the latest industry developments. Over time, with the skills and experience you develop and hone on this team, you can become an influential voice in defining where AI <> Fintech will be heading longer term.

Requirements

Strong SWE fundamentals (system design, APIs, etc.)
Hands-on experience working with LLMs to build products and shipping them to product with iterating with real user feedback - including but not limited to:
Prompt engineering
Fine-tuning
Retrieval augmented generation (RAG)
Semantic search - vector database and embedding models
Agent orchestration framework
Evaluation and monitoring framework of open-ended tasks
Streaming and SSE
Common UX and design patterns for GenAI-powered products
Strong debugging and monitoring experience for production systems
Ability to deeply understand customer and user needs through user research and rapid experimentation - be your own technical PM
Ability to balance divergent thinking (exploring possibilities) with convergent thinking (evaluating feasibility), which is critical for driving 0 ->1 projects
Extremely curious and passionate about working in GenAI applications space

Nice To Haves

Experience training and/or serving ML models in production, or fine-tuning LLMs for domain-specific use cases
Comfortable operating in privacy/PII-sensitive environments and applying compliance mitigations

Responsibilities

Work with other AI engineers, software engineers and machine learning engineers to architect, design and implement GenAI-powered products and features
Collaborate across functions to understand user needs, propose and implement AI-powered solutions where theyâre expected to have the highest impact
Design and execute rapid experiments to push the boundaries on potential business impact from emerging AI capabilities, with a focus on minimal viable testing approaches
Balance creative exploration of possibilities with rigorous evaluation of technical feasibility, product potential and business impact