At Lapel, we believe the most hospitable companies win. We're building the data, infrastructure, and tools that help customer-facing teams understand every customer and act on that insight at scale throughout the entire customer lifecycle.

Most internet businesses want to treat customers well, but their tools work against them. We’re building the layer that makes every interaction feel personal again: unifying context, coordinating action, and closing the gap between intent and experience.

It’s a technically ambitious challenge spanning data infrastructure, AI, and systems design – with a deeply human outcome: helping software companies serve people better. We call it hospitality at scale.

We’re well-funded, backed by top-tier investors, and already working with exceptional companies, from fast-growing startups to large enterprises. We’ll share more details when we talk.

About This Role

You’ll own the systems that power long-horizon agents at Lapel: how they remember, retrieve context, reason over customer history, use tools, take actions, and improve over time. This includes the memory, orchestration, observability, evaluations, and internal frameworks that make agents reliable across our products.

We believe that AI should be used to help scale the human moments and deliver exceptional service across any customer touchpoint. Your job is to build the infrastructure that makes that possible.

What You’ll Do

Design systems that help agents remember, summarize, and retrieve context across long-running customer interactions

Build workflows and tools that reduce failure modes and make agents easier to trust in production

Set up the observability and eval infrastructure we use to monitor agent behavior, debug issues, and raise quality over time

Help to scale multi-tenant systems that keep customer and agent context isolated, reliable, and ready for enterprise use

Our Stack

Lapel is built end-to-end in TypeScript in a single monorepo.

React, Vite, and Tailwind on the frontend;

Node.js and Postgres on the backend;

Temporal and Pulsar to power orchestration.

We ship quickly, and care about creating a developer experience where anyone can prototype, experiment, and get ideas into production fast.

You'll Thrive in This Role If You

Have 5+ years of backend or software engineering experience, with a track record of building reliable systems in production

Have owned large-scale, enterprise products end-to-end

Are excited to build the systems that make AI agents more useful, dependable, and easier to improve

Are comfortable designing APIs, working with databases, and building workflows that run reliably over time

Understand system design, debugging, observability, and production reliability at a practical level

Are customer-focused and able to translate product needs into reliable agent infrastructure

Enjoy working in fast-paced environments with high ownership and autonomy

Are proactive, detail-oriented, and take pride in building high-quality systems

The Process

We aim to hire with intention, respect your time, and offer feedback along the way.

Initial conversation (20 minutes) We’ll explore your motivations, share what we’re building, and answer your questions.

In-depth interview (60 minutes) We’ll go deeper into your craft and experience. No live coding. No prep work necessary.

Take-home project (2–4 hours) You’ll complete a short, well-scoped exercise that mirrors the kind of work you’d do here.

On-site visit (1–2 days) Join us in the office to review your take-home, grab lunch, and work on something real.

Compensation and Benefits

We believe great people build great companies – and we invest accordingly.

Competitive salary + meaningful early-stage equity

Full benefits: medical, dental, vision, 401(k), and commuter benefits

Generous time off: many company holidays and coordinated team-wide breaks throughout the year to focus and recharge

Office perks: lunch and dinner provided daily, in-office snacks, and health & wellness benefits

Team culture: regular off-sites, tight feedback loops, and a bias toward action