AI Data Engineer

Staff AI Data Engineer

Location: Remote (U.S.

-based) Preference given to CA, TX, and FL
Department: Product
Reports to: Chief Product Officer
Starting Pay Range: $184k - $215k/yr

Work Truck Solutions is the operating system for the commercial vehicle industry. While the retail auto market is saturated with software, the $130B+ commercial truck market was operating in the dark—until we turned on the lights.

We are the only platform that connects the entire ecosystem: OEMs, upfitters, dealerships, and fleet buyers. By digitizing complex inventory data (chassis + upfits) and streamlining the supply chain, we don’t just help dealers sell trucks; we ensure American businesses get the mission‑critical vehicles they need to work. We are a team of innovators, disruptors, and problem‑solvers dedicated to one mission: removing the friction from the commercial vehicle industry.

We are profitable, growing, and aggressively scaling our technology to remain the undisputed authority in this space.

We have spent years assembling something no one else in this industry has: a connected view of what’s being built, what’s sitting on lots, what’s moving, and what buyers are looking for and failing to find. Right now, a lot of that value is latent. It lives in our pipelines instead of in our customers’ hands.

We are seeking a Staff AI Data Engineer to change that. This role exists to turn our data asset into products dealers, upfitters, and OEMs will pay for and rely on—market intelligence, demand and pricing signal, inventory recommendations, automated enrichment, search and matching that actually finds the right truck.

Your primary job is delivering customer-facing results from our data. That said, you’ll need to be able to build the plumbing when it’s in your way. The best people for this role don’t wait on a ticket queue for a feature pipeline—they architect the ingestion and transformation they need, ship it, and hand it off cleanly. We’re looking for someone who is fluent in both directions and clear about which one the moment calls for.

We also want someone genuinely eager about what AI makes possible here. The hardest parts of our data problem—unstructured spec sheets, inconsistent upfit descriptions, entity resolution across feeds that agree on nothing—are exactly the problems where modern AI and LLMs are a step change over what was possible three years ago. You should be excited to reach for those tools, rigorous about proving they worked, and honest when a simpler method wins.

Rough shape of the role, so there’s no ambiguity about the emphasis:

• ~60% productizing data. Building models, analyses, and data products that reach customers—forecasting, pricing signal, recommendations, matching, enrichment, market intelligence—and iterating on them based on how they actually get used.
• ~25% AI-driven capability. Applying LLMs and ML to extract, structure, and enrich the data that makes those products possible, with the evaluation rigor to know it’s working.
• ~15% data engineering. Building the ingestion, transformation, and feature pipelines your work depends on, and setting standards others can follow.

Deliver results from our data

• Own customer-facing data products end to end: define the opportunity, build the model or analysis, ship it, measure whether it actually helped, and iterate.
• Build the intelligence layer of our platform—demand forecasting, pricing and market signal, inventory and configuration recommendations, matching and ranking—on problems where being right has direct commercial consequence for our customers.
• Partner with product and design on how model output surfaces to a dealer or upfitter, what happens when it’s…