Full-Stack Engineer

Zoomlogi•Chicago, IL

6d•$180,000 - $240,000•Hybrid

About The Position

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.

Requirements

2-7 years of professional engineering experience.
Genuinely full-stack: comfortable across backend, frontend, and APIs.
Production experience, shipped software with real users, with tests, code reviews, and CI/CD.
Strong in TypeScript/React and Python, or close enough to ramp quickly.
Understand data modeling and API design.
Based in San Francisco or Chicago and excited about being in the office.
Comfortable with ambiguity at the feature level.
Honest about what you don’t know.
Do not think customer contact is a tax on engineering time.
Willingness to switch contexts frequently across a wide surface area: workflow engine, detection layer, AI voice features, integrations, reliability.

Nice To Haves

Specific recent example of something you built, owned, and got in front of real users, and care about what happens after it ships.
Find the domain genuinely interesting (compliance constraints, quality/logistics tension, shipping medication to real patients).

Responsibilities

Own meaningful product areas from idea to release and back again, working closely with the founding team.
Build the data ingestion and prediction layer, ingesting streams from carriers, forwarders, IoT sensors, weather, and flight data.
Continuously scan for risk using ML and the mosaic view of each shipment to flag issues before they cascade.
Develop the AI workflow engine, including AI voice and text agents embedded in operational workflows.
Ensure every AI action is inspectable and recorded in an immutable audit trail (HIPAA, GDPR, SOC 2).
Focus on reliability, observability, incident response, performance tuning, and scalable patterns.
Build integrations with 50+ external systems, including carriers, IoT sensors, forwarders, telematics, weather, flight data, and customer ERPs.
Develop a framework that allows new integrations to ship in days, not weeks.