Engineering Manager Tlm Platform

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 build the team and the systems the rest of engineering depends on. When they compound, everyone gets faster. The Thesis Harper runs 200+ services across Railway and AWS. We orchestrate N parallel agentic workloads. Our AI systems make thousands of decisions a day, and every decision needs to be traceable, evaluated, and cheaper to run tomorrow than it is today. The infrastructure underneath all of that is the difference between a company that scales and one that stalls. Great platform engineering here isn't invisible—it's the reason product ships fast. When observability catches a silent failure before a customer does. When a pooling layer makes connection exhaustion a non-issue. When developer velocity doubles because someone built the tool nobody knew they needed. That leverage compounds across every other engineer on the team. The Role This role is open both to an experienced EM and to someone on the path to becoming an EM who doesn't yet have significant management experience—we're happy to bring that person on and support the transition. This is a team that still needs to be built. We have 2 people in it today, and we believe this area needs significant investment at Harper. The role owns three things: infrastructure (cloud, third-party vendors, and everything surrounding it), developer productivity (making sure we're genuinely an AI-first shop, with the right AI tooling and harnesses in place for every other engineering team to be successful), and SRE and production support (production discipline is top of mind as we head into a high-growth, high-scale phase—we need someone who has run an SRE function before). Each of these three areas can grow into a larger team of its own over time. The team is 2 people today, growing to five to six over the next three months. Because the team is small, whoever takes this role—EM or TLM—will need to be hands-on at the start, working directly with the team and doing the work themselves. What You'll Do Own infrastructure — cloud, third-party vendors, and everything surrounding it Build developer productivity — the AI tooling and harnesses that make every other engineering team genuinely AI-first Own SRE and production support — bring production discipline as we head into a high-growth, high-scale phase Build out the team — grow from 2 people to five to six over the next three months Stay hands-on — with a team this small, work directly alongside your engineers and do the work yourself, not ju