Full-Stack Engineer

About the role

WHO WE ARE

Nobody calls a logistics coordinator to say things went well. They call because a $100,000 shipment of clinical trial medication has been sitting in customs for three days and nobody can explain why. Because a biologic therapy arrived outside its temperature window and the treatment has to start over. Because the patient is waiting and nobody in the supply chain can give a straight answer about the shipment’s location.

This is the problem we’re solving. We’re a year in, tracking over 500,000 shipments (including for Fortune 100 customers), and we’ve already cut manual ops effort in half. General Catalyst, Eclipse Ventures, and Virtue led the seed. The angels are all former or current operators who've spent careers tracking down shipments themselves, such as Head of Logistics at Bristol Myers Squibb, CMO of Cardinal Health, President of Novo Nordisk US, President of UPS Air, and CEO of Uber Freight. They know the market is as large as the problem is broken.

What we’ve built works, and the most interesting problems are still ahead of us.

- Olivier, Co-founder & CEO

THE ROLE

This is a full-stack engineering role at a company that’s already in production, solving real-world problems with AI. The code you write will run on real shipments, for real customers, from day one.

Every engineer at ZoomLogi talks directly to customers. We believe engineers who’ve heard the problem firsthand make better product decisions than engineers who’ve read about it. You’ll be in customer calls regularly, because what you hear there will inform what you build.

The work spans the full stack. You’ll own meaningful product areas from idea to release and back again, working closely with the founding team. The engineer who joined last week was on a customer call and building AI features before the end of their first week.

WHAT YOU’LL BUILD

- The data ingestion and prediction layer. The platform ingests a continuous stream from carriers, forwarders, IoT sensors, weather, and flight data, stitched into a unified low-latency picture of every active shipment. We continuously scan for risk using ML and the mosaic view of each shipment to flag issues before they cascade. Getting detection to work reliably across hundreds of carriers, lanes, and sensor types is ongoing work, and the cost of a missed signal is measured in patient outcomes, not dropped requests.

- The AI workflow engine. Once a risk is detected, AI voice and text agents embedded in operational workflows prevent or resolve issues end to end, with human handoff when needed. Healthcare logistics requires every AI action to be inspectable and recorded in an immutable audit trail (HIPAA, GDPR, SOC 2). The hard part is the long tail: a carrier rep with a thick accent, a customs query that touches two playbooks, a status call that needs yesterday's exception as context. Closing that gap reliably, at low latency, with every action auditable, is most of the engineering work.