Founding Senior AI Engineer - Peerbound

Every B2B buyer wants to hear from a company's current customers before making a buying decision. As AI makes every market 10x more competitive, a company's ability to unlock and activate the voices of its happy customers isn't just a nice-to-have; it's the only moat. Peerbound makes this possible at scale.

Today, we do this for leading B2B SaaS companies like AlphaSense, Braze, Canva, Databricks, and Gong. We have the rare opportunity to disrupt not just multiple old-school software vendors, but entire categories of software, services, and labor.

Our core values are Dazzle Customers, Deliver Results, and Demand Excellence. We recruit people with a demonstrated track record of excellence and give them the room to do the best work of their lives. If this sounds like the environment where you do your best work, we invite you to build with us.

We are proud to be an NYC company, working hard in-person at our office near Penn Station. Our founders helped build Braze and Datadog, two of the city's biggest IPOs, and we're backed by the venture investors behind Square, Instacart, Airtable, and Webflow.

We've spent two years earning product-market fit the hard way: 30+ enterprise customers who pay us real money and keep doubling down. We just closed our seed round to scale the team and accelerate product development.

You'll be early enough to shape how this company builds software and know everyone by name on Day 1. But you're not walking into a blank canvas, as we have paying customers, a working product, and a validated roadmap. The risk is low and the leverage is enormous. Simply put, you will learn and contribute so much at Peerbound that you will accelerate your career more here than anywhere else.

Peerbound is a product-centered company. To create the most valuable product in the least time, we are dedicated to building an Engineering team of exclusively A-players, collaborating 4 days a week in our midtown Manhattan office.

AI features aren't an add-on at Peerbound; they're the reason customers pay us. Your job is to build AI features with the care and quality needed to make them feel magical. You'll report to our Head of Engineering and work closely with our CEO and customers, owning technical decisions, execution, and outcomes for your projects.

Build intelligence into every surface. Generate always-up-to-date customer profiles from call recordings, emails, surveys, reviews, and other touch points. Use website visitor metadata to dynamically prioritize which testimonials, reviews, and case studies appear on-screen. Build AI-powered surveys and interviews that tailor questions to match a customer's expertise to a live business need.

Ship constantly. Everyone in Peerbound Engineering is a maker, shipping features to customers regularly. You are equally likely to spend a week tuning prompts and models as you are designing database schemas or improving caching strategies.

Go deep, stay pragmatic. We are not afraid to go deep on ideas, but we lean hard into pragmatism. You'll own the full loop: research, prototype, build, ship, measure.

5+ years building features for a SaaS product, including 1+ years building LLM-based features. MS or PhD in engineering, statistics, or another quantitative field. Fluent in Python, statistics, and distributed system design. Real-world experience with LangChain/LangGraph, vector databases, and an eval framework.

$200,000 to $250,000/year plus meaningful equity and benefits (fully covered medical / vision / dental, 401K, flexible PTO). In-person in NYC, 4 days in-office.

This company represents my life's work. I've been part of two IPOs, and I want to build the kind of company at Peerbound where working here marks an inflection point in everyone’s career, and leads to the kind of bonds that last a lifetime.

This is our first dedicated AI engineering hire and is a sign of our commitment to AI as the core of our product forever. The right person won't just build features; they'll define how AI works at Peerbound for years to come, and their success will be the reason we invest more in this function.

If you're the kind of engineer who reaches for the simplest approach that actually solves the problem, who cares as much about eval rigor as model selection, and who wants to be somewhere early enough to shape the entire AI stack from the ground up, we'd love to meet you.