Own systems that run a business. Make architectural decisions that ship the same week. Build AI that compounds.

The Company

We’re on the frontier of building an AI-native professional services company. We’re building the best AI commercial insurance distribution engine for scale.

36 million businesses in America need insurance—it’s not optional. 77% are underinsured. 40% have no coverage at all. Over 90% of the industry is still human-led.

We’re building the inverse: 90%+ AI-led, inching toward the higher 90s. Not by automating tasks, but by capturing the decision traces—the exceptions, precedents, and cross-system context—that let AI make the same judgment calls humans do.

The Thesis

Every industry with human-bounded distribution consolidates rapidly once someone makes it computational. Search had thousands of engines until Google made ranking computational. Ride-hailing was fragmented until Uber made dispatch computational.

When distribution becomes computational, Jevons Paradox kicks in: increased efficiency leads to increased consumption. Better search didn’t mean less searching—it meant vastly more. Cheaper rides didn’t mean fewer trips—it meant transportation for use cases that never existed.

Insurance will follow the same pattern. When getting coverage becomes fast and frictionless, the 77% of underinsured businesses will finally get properly protected. The market expands, not contracts.

We’re building the engine that makes that happen.

The Role

You’ll be one of the most senior engineers at Harper. That means you own systems end-to-end, make architectural decisions that stick, and ship code that directly generates revenue.

This isn’t a “tech lead who attends meetings” role. You architect systems AND implement them. You mentor by building alongside people, not by reviewing PRs from a distance. You make decisions without waiting for consensus because we don’t have time for consensus.

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.

We’re a growing engineering team looking for high-agency operators who can own entire systems.

What You’ll Own

Core AI infrastructure. The systems that give our AI intelligence—LLM orchestration, prompt management, retrieval systems, structured output parsing. When someone asks “why did the AI make that decision?” you’ll own the systems that answer that question.

Agent architecture. Today we have agents for sales, customer service, and underwriting. But the patterns are ad-hoc. You’ll design the abstractions that let us ship new agents in days—defining how agents share context, hand off to each other, and escalate to humans.

Evaluation that actually works. LLM evaluation is hard. Most companies ship vibes. We need systems that measure whether our agents are getting better at closing deals, answering questions accurately, and catching edge cases. You’ll own the infrastructure that makes this rigorous.

Decision trace capture. Every judgment our AI makes needs to be traceable—what context it had, what precedents it considered, what outcome resulted. This is the data that lets us compound. You’ll architect how we capture, store, and query millions of decision traces.

Platform scale. Thousands of concurrent conversations, millions in premiums processed, sub-second response times. You’ll own reliability for systems where downtime means lost revenue.

You Might Be a Fit If…

You’ve owned production systems. Not “contributed to”—owned. You were the person who got paged when it broke. You made the decisions when things were ambiguous.

You architect AND implement. Some senior engineers can only draw diagrams. Some can only write code. You do both, and you know that the best architecture comes from people who have to live with what they build.

You’ve built AI systems in production. LLM applications, agent frameworks, RAG pipelines, voice AI—you’ve shipped AI that serves real users at scale.

You write code with AI. Cursor, Claude Code—you manage multiple coding sessions simultaneously. You’ve figured out how to multiply your output.

You’re 3-6 years into your career. Senior enough to own systems, recent enough to still be hands-on every day.

Compensation

Salary: $160,000 - $230,000

Equity: 0.05% - 0.30%

Location: San Francisco, in-office. We build together.

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

The Super Day: You’ll show how you rapidly gain context on a new codebase, use AI code generation to architect and ship, and manage multiple coding sessions simultaneously. We assess system design, evaluation strategy, and how you build under ambiguity.

To Apply

We’re building a vertically integrated AI platform that connects go-to-market, sales operations, customer service, and retention under one architectural roof. That integration creates compounding through feedback loops—every interaction makes the system smarter. Thousands of businesses already trust us.

If you want to own systems that run a real business, work with founders who will actually listen to you, and build something that’s never existed—send your resume and tell us about a system you’ve owned.

We’re a championship-minded team. We push each other. We move fast. We care about craft. If that sounds like where you belong, let’s talk.