Senior Director, Engineering (Customer Experience)

About Birdeye Inc.

Birdeye builds AI coworkers for multi-location brands. Jay for marketing, Myna for operations, and Robin for customer experience orchestrate AI agents to win AI visibility, book appointments, and drive consistent CX at every location. They run on BirdAI, the Agent OS built on 13 years of multi-location data infrastructure and 3,000+ integrations. BirdAI consolidates every signal, reasons with industry, brand and location context, and acts autonomously or with a human in the loop.
Birdeye is trusted by the world's largest multi-location brands across healthcare, dental, restaurants, financial services, auto, retail, and property management. G2 ranks Birdeye #1 in Enterprise Multi-Location Marketing Platforms and named it to the 2026 Best Agentic AI Products list. Learn more at birdeye.com.

About the role

Birdeye is evolving from a suite of individual products into a unified, AI-native platform. We are not just adding AI features; we are building the platform and agentic layer that lets businesses automate complex customer interactions, reason over their own data, and execute multi-step work across the customer lifecycle.

You will lead the engineering organization behind one of Birdeye’s core product lines — the platform foundations, high-scale services, and AI capabilities that power it. You will own it end to end: architecture, roadmap, delivery, reliability, and the business outcomes it drives, for a product used by tens of thousands of businesses every day. This is a hands-on leadership role: you lead through managers and senior ICs, but you stay close to the architecture, the tradeoffs, and the hard problems.

What you'll do

• Own the product line end to end. Set the technical strategy and multi-quarter roadmap in partnership with Product and Design; ship initiatives that move core company metrics — adoption, retention, performance, and cost-to-serve.
• Architect for scale. Guide the evolution of your product line into scalable, multi-tenant platform services — data pipelines (streaming and batch), retrieval and indexing, event-driven systems, and high-throughput APIs — that other product and engineering teams build on top of.
• Bring AI into production. Drive LLM-powered and agentic capabilities from prototype to GA — orchestration, tool use, retrieval, and context — with eval-driven development, guardrails, and feedback loops that improve quality and reliability against real customer data.
• Operate with a high reliability bar. Own SLOs, deep observability, healthy on-call, and prevention-first incident and post-mortem practices for services enterprise customers depend on most; treat reliability and operational excellence as a competitive advantage, not an afterthought.
• Scale and shape the organization. Hire, coach, and retain a high-performing team; grow your managers and senior ICs, shape team structure and ownership, and raise the technical bar continuously.
• Partner across the company. Work as a peer to Product, Design, and GTM; translate real customer and business needs into a defensible engineering roadmap, and represent your product line and its tradeoffs in cross-functional and executive settings.

What you'll bring

• You’ve led larger engineering organizations. 12+ years in software engineering and 5+ years leading larger organizations — including other managers and senior individual contributors — ideally across both startups and larger enterprises. You’ve scaled teams through real change, not just steady-state growth, and brought them through ambiguity without losing their trust or their output.
• Worked on and scaled Customer Experience products including Surveys, Insights, Reporting, and Ticketing.
• You have deep technical judgment and an IC foundation. A technically leaning leadership style: you stay close to the code and the design, can credibly debate architecture and tradeoffs with senior ICs, guide architecture, debug complex failures, and raise the technical bar of your team.
• You’ve built distributed systems at scale. Hands-on experience building and operating distributed systems, execution engines, or real-time platforms where correctness and reliability matter — large-scale data/indexing systems, retrieval, high-QPS multi-tenant services — with strong systems judgment on scalability, performance, security, and build-vs-buy. You’ve shipped systems that had to be fast, cheap, secure, and reliable at scale, not just functional.
• You’ve shipped AI in production. You’ve built and scaled features on generative AI for demanding users and understand the nuances of AI product development — evals, guardrails, and iteration loops — not just prototypes.
• You own a product line, not a component. A track record of shipping best-in-class products (consumer or enterprise) that drive significant user and business value, with clear ownership of both delivery and team health.
• You raise reliability as you scale. You’ve built strong service-ownership models, clear on-call practices, and durable quality bars in fast-growing orgs, and used data to make real investment tradeoffs between reactive and proactive work.
• You’re a strong communicator and partner. You develop engineers and engineering leaders, communicate clearly (in writing by default) to align technical and non-technical stakeholders, and partner deeply with Product and Design while bringing an independent engineering point of view.

AI-first mindset

We expect you to lead AI-native engineering, not just build AI-native products. You use AI coding agents and orchestration tools in your own work, and you have a concrete point of view on how they change what “good” engineering practice looks like — code review, on-call ownership, and quality bars all need rethinking when a meaningful share of code is agent-authored. You embed accountability into AI-assisted workflows rather than lowering the bar for velocity, and you expect the same from your teams. As part of the interview process, you’ll walk us through how you use AI tools today to drive impact for you and your team.