Job description

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. - Reliability. We’ve tracked 500,000 shipments for customers who can’t afford downtime. Observability, incident response, performance tuning, and the scalable patterns that hold as volume grows: unglamorous work that matters as much as anything else on this list. - Integrations. The platform connects to 50+ external systems today: carriers, IoT sensors, forwarders, telematics, weather, flight data, customer ERPs. Most don't have clean APIs. Some claim a shipment is delivered while the GPS shows it still in transit. Some go silent without warning. The work is building integrations that hold up against that messiness, with a framework that lets the next one ship in days, not weeks. Every new customer brings two or three of their own. WHO THRIVES HERE - You have a specific recent example of something you built, owned, and got in front of real users. And you care about what happens after it ships. - You’re comfortable with ambiguity at the feature level. The most interesting problems here don’t come with detailed specs: you hear something from a customer, you figure out what to build, and you build it. If you prefer a PM to define the problem before you start, this role will be frustrating for you. - You find the domain genuinely interesting. The compliance constraints, the quality/logistics tension, the fact that what you ship is moving medication to real patients: these should feel like compelling design constraints, not obstacles. An engineer who finds regulated industries tedious won’t do their best work here. - You’re honest about what you don’t know. We debate hard, change our minds, and challenge each other, but always with the assumption that everyone in the room is trying to get it right. - You don’t think customer contact is a tax on engineering time. Every engineer here is in customer calls. The product decisions that come out of those calls are the ones worth making. This role is probably not right for you if you prefer to go deep on one hard technical problem and be left alone. The surface area here is wide: workflow engine, detection layer, AI voice features, integrations, reliability. If switching contexts drains you, this will too. WHAT WE’RE LOOKING FOR You have 2-7 years of professional engineering experience. You're genuinely full-stack: comfortable across backend, frontend, and APIs. You have production experience, you’ve shipped software with real users, with tests, code reviews, and CI/CD. You’re strong in TypeScript/React and Python, or close enough to ramp quickly. You understand data modeling and API design. You’re based in San Francisco or Chicago and excited about being in the office. THE TEAM Founded by a team of operators with deep experience in Logistics Tech & Healthcare (Ex-Uber Freight GM + Airspace CRO), joined by early engineers from the likes of Uber, Hippocratic AI, Assort Health and others. We’re here to solve a real-world problem at scale, by making every critical shipment visible, predictable, and on time, so potentially life-saving therapies reliably reach the people who need them. THE STACK Python, React/TypeScript, Kafka, AWS. We do a lot of low-fidelity prototyping, Google Slides included. LLM infrastructure and orchestration are increasingly central to how we build. THE INTERVIEW PROCESS We move quickly. - Intro call: role fit, motivation, what you’ve shipped - Technical screen: a realistic problem close to the actual work - Onsite: we’ll build something together - References → Offer COMPENSATION & LOGISTICS - Base salary: $180K-$240K - Equity: Above market (4-year vesting, 1-year cliff) - Benefits: Best-in-class medical and dental (100% self, 50% dependents) - Location: San Francisco HQ (Mission St). Hybrid, 4 days/week in office.

Requirements

Must-haves

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.
You have a specific recent example of something you built, owned, and got in front of real users. And you care about what happens after it ships.
You’re honest about what you don’t know. We debate hard, change our minds, and challenge each other, but always with the assumption that everyone in the room is trying to get it right.
You don’t think customer contact is a tax on engineering time. Every engineer here is in customer calls. The product decisions that come out of those calls are the ones worth making.
Python, React/TypeScript, Kafka, AWS. We do a lot of low-fidelity prototyping, Google Slides included. LLM infrastructure and orchestration are increasingly central to how we build.
We move quickly.
Intro call: role fit, motivation, what you’ve shipped
Technical screen: a realistic problem close to the actual work
Onsite: we’ll build something together
References → Offer
Base salary: $180K-$240K
Equity: Above market (4-year vesting, 1-year cliff)
Benefits: Best-in-class medical and dental (100% self, 50% dependents)
Location: San Francisco HQ (Mission St). Hybrid, 4 days/week in office.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlgoci/cdtypescriptreactpythonapiawsllm

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental