Forward Deployed Engineer - Stealth AI Freight Operations Startup

UP.Labs•Bentonville, AR

23h•Onsite

About The Position

Freight is the lifeblood of the global economy, yet the operations that keep it moving are still largely reactive and manual - endless phone calls, spreadsheets, fragmented systems, and constant hand-offs under the pressure of disruptions no one saw coming. The result is wasted capacity, missed appointments, and margins that erode with every exception. We are a stealth-stage company building an AI platform that turns reactive freight operations into predictable, optimized outcomes - using data science, machine learning, and agentic orchestration to identify issues, automate actions and optimize operations. We are early and backed by both VC and a Fortune 500 company, and assembling a founding team to take this from zero to one. We are looking for a Forward-Deployed Engineer (FDE) to turn our technology into measurable value on the ground. Embedded alongside dispatchers, brokers, and operations leaders, you will translate their hardest problems into working software and ship it fast. You are one of the first people our customers meet and the person who makes our platform work in their world. Expect to spend time in the field learning how operations really run, then move fast to build integrations, configure agents, validate outputs, enable adoption and stand up production solutions - carrying everything you learn straight back into the core product. This posting is intentionally open on level: we hire for the skill set and bias to action, not a title. Candidates from senior IC through founding-level will be considered, with scope and compensation matched to experience.

Requirements

Strong, pragmatic engineering - you write clean, production-quality code and can move comfortably across the full stack.
Systems thinking - you understand how APIs, data, and workflows connect, and you can architect an end-to-end solution from a vague problem statement.
Deep fluency with production AI - you have direct experience architecting, testing, and refining agentic workflows and LLM systems. You bridge the trust gap between an impressive demo and a hardened production solution by mastering prompting, tool-use, evals, and rigorous trace analysis.
Customer obsession - you are energized by sitting with a customer, diagnosing a broken process, and shipping something that fixes it. You explain technical ideas clearly to non-technical stakeholders.
0-to-1 bias to action - comfortable with ambiguity, missing requirements, and shifting priorities. You make progress without a spec and a safety net.
Ownership and presence - willingness to be on-site in Northwest Arkansas and to travel to customer locations as needed (typically up to ~25%).

Nice To Haves

Experience in freight, logistics, supply chain, or another operations-heavy enterprise domain.
Familiar with data pipelines and integrating into large enterprise platforms — event streams (Kafka), data warehouses (BigQuery, Snowflake), and TMS via API/EDI.
Prior experience as a forward-deployed engineer, solutions engineer, or founding engineer at an enterprise-software or AI company.
Experience standing up data pipelines and ML or optimization workflows in production.

Responsibilities

Spend real time on-site and embedded with customers to understand their freight workflows, systems, and pain points firsthand.
Design and build integrations, data pipelines, and agentic workflows that connect our platform to TMS, ELD, EDI, and the long tail of customer systems.
Deliver early wins that prove value within weeks, then iterate toward scaled, production-grade deployments.
Ingest, clean, and structure messy operational data so our models and agents can act on it reliably.
Author, test, and tune prompts and agent logic; monitor production systems and own their reliability, accuracy, and performance.
Champion the user experience by bridging the gap between agent performance and real-world results, ensuring stakeholders trust and rely on the platform for day-to-day operations.
Carry hard-won lessons from the field back to product and engineering, shaping the roadmap and turning one-off solutions into reusable platform capabilities.
As one of our first FDEs, help define what forward-deployed engineering looks like here: the playbooks, tooling, and culture of the function.