The Problem

36 million businesses in America need insurance—it's not optional. 77% are underinsured. 40% have no coverage at all. The distribution system failed them: too slow, too opaque, too confusing.

Over 90% of commercial insurance is still human-led. We're building the inverse: 90%+ AI-led, pushing toward the higher 90s. Not by patching legacy workflows—by building AI that makes humans more effective, improves the customer experience, and eliminates friction at every step.

We're adding ~1,000 customers per month. We've grown 100x since last year. We're looking to do even more this year—and that's why we're hiring.

The hardest entry-level job in tech—and the one that teaches you the most.

The Thesis

To build AI that actually works, someone has to understand the work at a molecular level first—every exception, every edge case, every decision that currently lives in someone's head. That someone is you. Every call you take becomes training data that makes our AI smarter. Every pattern you flag shapes the product. You're not adjacent to the AI—you're the reason it gets better.

This is the real version of "getting into AI." Not watching from the sidelines. Doing the work that makes the system work.

The Role

Your primary function is to speak to real business owners to identify patterns across hundreds of conversations, independently determining which edge cases are signal vs. noise, and translating what you learn into structured training data and rules engine specifications that shape how our AI operates. You conduct intake calls to generate that intelligence firsthand because there is no substitute for doing the work yourself to understand it at the depth this role requires.

You'll work in close partnership with sales, operations, and engineering to help determine what gets automated next, how an edge case should be classified, whether an AI output is acceptable or needs correction. Your recommendations have direct product consequences. The company moves on what you surface.

High call volume is part of the operating context. The intensity is real, and we won't pretend otherwise.

What You'll Do

• Conduct intake conversations — Talk with business owners across every industry, capturing information accurately and at volume; this is the primary mechanism for generating the firsthand intelligence your analysis depends on

• Analyze conversation patterns and determine what they mean — Evaluate intake calls to identify recurring edge cases, failure modes, and coverage knowledge gaps; decide which patterns are material enough to affect AI training or product design

• Label and classify AI training data — Tag edge cases, flag unusual patterns, and categorize inputs so engineers can act on them; your judgment about what's anomalous vs. routine is what makes the training data useful

• Pressure-test and refine what engineers build — Review rules engine logic against the edge cases you've seen firsthand; determine whether the system handles real-world variation correctly and surface gaps before they reach production

• QA voice AI agents against real-world standards — Listen to AI-handled calls, flag where the agent got it wrong, and document what good looks like based on what you've experienced firsthand; your feedback is what engineers use to calibrate

• Document and systematize domain knowledge — Convert institutional knowledge and call experience into structured frameworks, playbooks, and training materials that scale across the team

You Might Be a Fit If...

• You're a recent grad or early in your career—but you think analytically, not just executionally

• You notice patterns in repetitive work that others miss, and you act on them

• You learn fast and take ownership without being asked

• You're genuinely interested in how AI systems are built and trained, not just what they do

• Talking to people all day energizes you—it doesn't drain you

• You want career progression into product, engineering, or operations—and you understand this is how you earn it

Requirements

• Bachelor's degree

• Strong analytical and pattern-recognition skills—you don't just do the work, you evaluate it

• Strong communication skills—clear, professional, and confident on the phone

• Typing speed and multitasking ability (talk, type, and navigate systems simultaneously)

• Comfort with technology and willingness to learn new tools quickly

• Based in San Francisco or willing to relocate

Nice to Have

• Any experience that required recognizing patterns in volume data—research, analytics, operations, or customer-facing roles

• Bilingual (Spanish)

• Interest in startups, insurance, or fintech

• Business, finance, communications, or operations coursework

Compensation

• Salary: $85,000–$125,000 DOE + performance bonuses

• Location: San Francisco, in-office

Benefits

• Health, dental, and vision insurance

• Commuter benefits

• Team meals and snacks

The Process

1. AI assessment / Take-home assignment — Mock intake call with an AI agent

2. Team lead screen — Skills and culture fit

3. Super day — Meet the team and see how you operate in real time

To Apply

This is the fastest way into tech—and the best one. If you want to learn an entire industry, work at an AI company that's actually shipping, and grind your way into a real career—apply.

Compensation Range: $85K - $125K