Harper - Platform Engineer
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 • 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
Responsibilities
• Own core infrastructure — Databases, AWS services, networking, CI/CD; the systems every engineer depends on • Own core infrastructure • Build for scale — Thousands of concurrent AI operations, sub-second response times, cost-effective under load • Build for scale • 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 • Design observability that works • Orchestrate agentic workloads — N parallel agent instances doing non-deterministic work, running reliably and cost-effectively as N grows • Orchestrate agentic workloads • Multiply developer velocity — The tooling and abstractions that let product engineers ship in days instead of weeks • Multiply developer velocity • Own reliability end-to-end — SLOs, on-call, incident response, and the post-mortems that make it rarer next time • Own reliability end-to-end • Build the eval infrastructure — Systems that measure whether AI outputs are getting better, not just running • Build the eval infrastructure • 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
Benefits
• $140K – $280K • Offers Equity • Offers Bonus • 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. • 77% are underinsured. • 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. • Salary: $140,000–$280,000 depending on experience + performance bonuses & equity • Location: San Francisco, in-office. Based in SF or willing to relocate. • Location • Schedule: Monday–Friday, very early morning start, in-office five days a week. • Schedule • Benefits: Uber commuter benefits; breakfast, lunch, and dinner provided; snacks, drinks, and coffee daily; free gym membership; health, dental, and vision insurance. • The Process • Technical screen — 60 min remote: project deep dive + system design • 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.
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