About Raindrop

Raindrop is the monitoring platform for AI agents. Engineering teams at Fortune 100s and the fastest-growing AI companies (Vercel, Speak, Clay) use it to catch silent failures in production. When an agent misbehaves, Raindrop alerts the team, links to the trace, and surfaces the root cause so they can fix it fast. With Raindrop 2.0 (Self-Healing Agents), the loop closes itself: your coding agent fixes it, and the fix becomes an eval to prevent regressions.

Why It Matters

AI agents fail constantly in ways both hilarious and terrifying. Regular software throws exceptions. But AI agents fail silently, leaving engineers with almost no visibility into how their agents are actually performing.

The current status quo is sifting through millions of logs and trying to debug flaky evals that just aren't matching real world results. Evals are like unit tests, they confirm your model got specific test cases right. But in the real world agents call thousands of tools, run for hours, and encounter millions of unpredictable actions.

That’s where Raindrop comes in. It learns the unique shape of each AI agent’s issues. Starting from presets like Laziness, Forgetting, or Task Failure, to automatically tuning itself to each agent and discovering unknown unknowns.

With one click of a button, AI engineers start tracking issues or topics across 100% of their production data. They can see frequency over time, how many users are affected, relevant properties and more.

In order to process hundreds of millions of events, we gradually train small, custom models, private to each company, that learn to uniquely understand how their product is used.

As part of the early team, you’ll play a fundamental role in shaping the company - from making strategy and product decisions, to helping scale the team, to shaping the future of AI agents.

Our Investors

We've raised $50M total, led by CRV, with backing from Lightspeed, Figma Ventures, Vercel Ventures, and the founders of Replit (Amjad Masad, Michele Catasta), Cognition (Walden Yan), Framer (Koen Bok, Jorn van Dijk), Speak (Andrew Hsu), and Notion (Akshay Kothari).

About the role

We're looking for our first Sales Engineer to be the technical partner to our early sales team. You'll run technical discovery, demo Raindrop to engineering teams, and build POC's to close deals. As our first Sales Engineer, you'll build how we sell technically from the ground up and ensure it's a scalable journey along the way.

Your Focus

• Technical discovery and demos: Learn how a customer's agents work and where they fail silently, then show them how Raindrop catches it and their path to correcting it.

• Proofs of concept: Run evaluations end to end, and make sure customers see value before they sign.

• Turning customer asks into product: Spot when a customer request points to something many customers need. Prototype it, then help it become part of the platform.

• Building the function: Create the demos, playbooks and technical content the sales team will use.

Your responsibilities

• Partner with AEs from discovery to close and own the technical side of the deal.

• Build conviction with technical buyers by answering their hardest questions and showing how Raindrop solves their problems.

• Build prototypes and integrations for customer requests. Ship them into the product when you can.

• Know when a request needs a deeper backend or ML change, and bring in the right engineers.

• Bring patterns from deals back to the team, along with an opinion on what to build.

• Work closely with leadership and own high-impact work from day one.

Ideal Candidate

• Top 0.1% communicator who can win over a room of skeptical engineers.

• Strong engineer who can read a codebase, build a working prototype in days, and earn our engineers' trust.

• Sales instincts: you know how deals move, and when to lead and when to let the AE run the room.

• You've sold a developer tool or infrastructure product to engineers, ideally as an early SE who built the playbook.

• You make problems disappear. Customers bring you something messy, and you come back with it solved.

• You understand how agents fail in production and can explain it to the people building them.

• Use and build AI agents and tools