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 build 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

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.

What You'll Do

• 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 Might Be a Fit If...

• 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

Requirements

• Software engineering experience with a platform, infrastructure, or SRE focus (level determined during interviews)

• Proficiency in Python, TypeScript, Go, or similar

• Production experience with cloud infrastructure (AWS), databases (Postgres), and observability tooling

• Track record of building systems and tooling that other engineers depend on

• Based in San Francisco or willing to relocate

Nice to Have

• Experience orchestrating AI/ML workloads at scale (agent frameworks, LLM inference infrastructure)

• Voice AI or real-time systems experience

• Deep AWS experience (ECS, Lambda, RDS, VPC design)

• Prior startup experience—especially at companies that scaled through hypergrowth

Compensation & Logistics

• Salary: $140,000–$280,000 depending on experience + performance bonuses & equity

• Location: San Francisco, in-office. Based in SF or willing to relocate.

• Schedule: Monday–Friday, very early morning start, in-office five days a week.

• Benefits: Uber commuter benefits; breakfast, lunch, and dinner provided; snacks, drinks, and coffee daily; free gym membership; health, dental, and vision insurance.

The Process

1. Technical screen — 60 min remote: project deep dive + system design

2. Super Day on-site — meet the team, sit in on the operation, do real work alongside us.

    

To Apply

If you want to build AI capabilities against business problems you discover yourself, ship code that runs a real business, and work with people who show up with the same intensity every day—send your resume and a link to something you've built that had measurable business impact.