Trace every run. Auto-fix silent failures. Simulate before shipping. Raindrop helps you detect agent issues and prevent them from happening. Backed by Lightspeed and Y Combinator.
About the role
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.
What they're looking for
- Proven ability to generate cold pipeline: meetings booked from accounts with no prior relationship, in markets where your network doesn't help
- Exceptional written communication, your emails and DMs are the first impression of Raindrop
- Builds with AI, not just uses it: agents, enrichment pipelines, Clay-style workflows that make one person prospect like a team of five
- Technically curious: genuinely wants to understand how AI agents work, what a trace is, and why silent failures matter
- Experience prospecting into engineers, technical leaders, or developer audiences, or the instincts to know why devs hate typical sales outreach
- Resilient, self-motivated, and consistent. Outbound is a volume game played with precision
More about this role
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.
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...
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