Platform Engineer

The Problem

36 million businesses in America need insurance—it's not optional.
77% are underinsured. 40% have no coverage  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 build the systems the rest of engineering depends on. When they compound, everyone gets faster.

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.

You're an infrastructure-focused engineer with depth across databases, AWS, networking, observability, SRE, CI/CD, and AI tooling. You build the systems and tooling the rest of engineering depends on—optimized for performance, reliability, and developer velocity.

You work directly with founders and alongside the product engineers who ship features on top of what you build. You see where things break at scale, design the fix, and ship it before it becomes a fire. When something goes wrong at 2 AM, you're the one who gets paged—and you'd rather build the system that prevents the page than accept it as inevitable.

We are hiring platform engineers across all levels, which we determine during the interview process.

• Own core infrastructure — Databases, AWS services, networking, CI/CD; the systems every engineer depends on

• Build for scale — Thousands of concurrent AI operations, sub‑second response times, cost‑effective under load

• Design observability that works — System‑level errors AND business‑level silent‑failure detection; we find out something is broken from an alert, not from a person hours later

• Orchestrate agentic workloads — N parallel agent instances doing non‑deterministic work, running reliably and cost‑effectively as N grows

• Multiply developer velocity — The tooling and abstractions that let product engineers ship in days instead of weeks

• Own reliability end‑to‑end — SLOs, on‑call, incident response, and the post‑mortems that make it rarer next time

• Build the eval infrastructure — Systems that measure whether AI outputs are getting better, not just running

• You've owned production systems at scale—not contributed to, owned; you got paged when they broke

• You write code with AI (Cursor, Claude Code) and know how to leverage it for infrastructure work

• You have depth across at least two of: databases, cloud infrastructure (AWS), networking, observability, or CI/CD

• You've designed systems that survive real load—retries, DLQs, back pressure, idempotency, the whole stack

• You care about developer experience—the tools other engineers use are as much your product as the customer‑facing ones

• You'd rather build the system that prevents the fire than firefight forever

• Software engineering experience with a platform, infrastructure,…