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.
You’ll be one of the most senior engineers at Harper. That means you own systems end-to-end.
The Thesis
The difference between MTS and Senior MTS: you own outcomes, not tasks. You look at a business problem—“we need to 10x our quoting capacity”—and figure out what to build, build it, and make sure it works. You architect systems AND implement them. You mentor by building alongside people, not by reviewing PRs from a distance.
The Role
This isn’t a “tech lead who attends meetings” role. You make architectural decisions that stick, and ship code that directly generates revenue. No waiting for consensus—we don’t have time for consensus.
What You’ll Do
Own core AI infrastructure — LLM orchestration, prompt management, retrieval systems, structured output parsing
Design agent architecture — Define abstractions that let us ship new agents in days
Build evaluation that works — Systems that measure whether agents are getting better at closing deals
Architect decision trace capture — Make every AI judgment traceable
Own platform scale — Thousands of concurrent conversations, sub-second response times
You Might Be a Fit If…
You’ve owned production systems (not contributed to—owned; you got paged when it broke)
You architect AND implement—the best architecture comes from people who live with what they build
You’ve built AI systems in production (LLM applications, agent frameworks, RAG, voice AI)
You write code with AI (Cursor, Claude Code) and manage multiple sessions
You’re 3-6 years into your career
Requirements
3-6 years software engineering experience
Production experience with LLM applications and AI systems
Strong architectural skills with hands-on implementation
Track record of owning systems end-to-end
Based in San Francisco or willing to relocate
Nice to Have
Voice AI or real-time systems experience
Experience building agent frameworks or evaluation systems
Prior startup experience
Compensation
Salary: $160,000–$230,000 + performance bonuses & equity
Location: San Francisco, in-office
Benefits
Health, dental, and vision insurance
Commuter benefits
Team meals and snacks
The Process
15-min founder call — Alignment on mission and pace
If in SF: Super Day on-site
If outside SF: Technical phone screen, then on-site
To Apply
If you want to own systems that run a real business and work with founders who will actually listen to you—send your resume and tell us about a system you’ve owned.