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Engineering Manager, AI Applications

Lead a team of 4 engineers building AI applications and LLM-powered products at Plaid. Drive strategy for AI integrations, customer support agents, and agentic commerce systems.

216k – 367kSan Francisco, CAEngineering ManagementHybrid8+ YOE

About the role

Responsibilities

  • Lead a team of 4 engineers (junior to staff level) through goal setting, coaching, and feedback
  • Define and drive long-term strategy and manage execution in partnership with technical and product leaders
  • Lead projects enabling and scaling business with largest AI customers and partners, starting with personal finance use cases
  • Develop and evolve preferred integration patterns for Plaid with AI providers (API adaptations, official Plaid MCP Servers)
  • Redefine how Plaid's consumer link experience embeds into conversational interfaces
  • Architect the trust layer for agentic commerce
  • Scale and extend AI-powered customer experience systems
  • Improve homegrown customer support agent with multi-turn and multi-agent systems
  • Define scalable offline evaluation for complex multi-turn open-ended tasks
  • Research and prototype Human-In-The-Loop Reinforcement Learning (RLHF) for insights flywheel
  • Pioneer architecture for customer-specific long-term memory
  • Extend agentic systems to support customer journey areas: product recommendation, onboarding, risk diligence, activation, assistance, upselling, cross-selling

Requirements

  • 8+ years of industry experience including time as a Staff-level engineer before transitioning to management
  • 1+ years of engineering management experience
  • Hands-on experience working with LLMs to build and ship products with real user feedback, including:
    • Prompt engineering
    • Fine-tuning
    • Retrieval augmented generation (RAG)
    • Semantic search (vector databases, embedding models)
    • Agent orchestration frameworks
    • Evaluation and monitoring frameworks for open-ended tasks
    • Streaming and SSE
    • Common UX and design patterns for GenAI-powered products
  • Ability to deeply understand customer and user needs through user research and rapid experimentation
  • Track record of building and growing high-performing engineering teams
  • Ability to balance divergent thinking (exploring possibilities) with convergent thinking (evaluating feasibility)
  • Extremely curious and passionate about GenAI applications

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

Skills

LLMsPrompt EngineeringFine-TuningRAGVector DatabasesEmbedding ModelsAgent OrchestrationEvaluation FrameworksStreamingSse

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