What are the responsibilities and job description for the Machine Learning Engineer position at Raindrop?
Raindrop is building Sentry for AI Agents.
Engineering teams at companies use Raindrop to get alerts about silent failures with their AI agents. Raindrop sends alerts when AI agents misbehave and links straight to the events, so AI engineers can dig into the conversations or traces, understand the root cause, and fix it, fast.
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 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.
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’re backed by incredible investors, including Lightspeed and leading AI companies, including Figma Ventures, Vercel Ventures, founders of Replit (Amjad Masad and Michele Castata), Cognition (Walden Yan), Framer (Koen Bok and Joen van Dijk), Speak (Andrew Hsu), Notion (Akshay Kothari), and more.
Your Focus
Engineering teams at companies use Raindrop to get alerts about silent failures with their AI agents. Raindrop sends alerts when AI agents misbehave and links straight to the events, so AI engineers can dig into the conversations or traces, understand the root cause, and fix it, fast.
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 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.
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’re backed by incredible investors, including Lightspeed and leading AI companies, including Figma Ventures, Vercel Ventures, founders of Replit (Amjad Masad and Michele Castata), Cognition (Walden Yan), Framer (Koen Bok and Joen van Dijk), Speak (Andrew Hsu), Notion (Akshay Kothari), and more.
Your Focus
- Build out a world-class product - servicing millions of requests a day growing.
- Architect, implement, and scale ML pipelines
- Quick iteration without compromising on quality
- Deeply understand the customer
- Knows how to balance short-term and long-term speed
- Proven experience scaling applications
- Interest in AI products tools (ideally experience building these or an avid user)
- Growth mindset
- Cares about building well-designed products
- Willing to do whatever it takes to solve a problem
- Must be in person in San Francisco (or willing to move)
Salary : $150,000 - $250,000