ML Engineer

Uncover; San Francisco, Northern (CA, KY); Hybrid; USD 150,000 - 210,000; Full time

Job summary

Raindrop is building a monitoring platform for AI agents and is looking for team members to help scale ML pipelines and the product to serve millions of requests daily. As part of the early team, you’ll help shape strategy, scale the team, and influence the future of AI agents.

The ideal candidate balances speed with long-term quality, has a strong interest in AI products, and is willing to relocate or be in person in San Francisco.

Qualifications

• Experience scaling applications and systems.
• Interest in AI products and tools (ideally experience building these or an avid user).
• Growth mindset and willingness to take on challenging problems.
• Ability to balance short-term delivery with long-term product speed, in a fast-moving environment.

Responsibilities

• Build out a world-class product servicing millions of requests daily and growing.
• Architect, implement, and scale ML pipelines with high quality and speed.
• Iterate quickly without compromising on product quality.
• Develop a deep understanding of customer needs and use insights to inform product decisions.

Skills

Scaling applications

Growth mindset

Willing to relocate

Customer understanding

AI product interest

Job description

About RaindropRaindrop

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

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 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

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

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 Jorn 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 pipelinesQuick iteration without compromising on qualityDeeply understand the customer.

Ideal Candidate

• 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)