Overview
A fast-growing AI data company is hiring supply chain practitioners to design expert-level Terminal-Bench tasks for evaluating AI agents. You will turn real planning work into end-to-end challenges, each with files, a working solution and an automated correctness check. The goal is to build a benchmark grounded in real operations work.
***THIS IS A TASK BASED COMPENSATION MODEL. PLEASE CHECK THE APPLICATION QUESTIONS FOR MORE DETAILS.***
Responsibilities
- Design expert-level Terminal-Bench tasks by turning a real supply chain task from your work into a challenge an AI agent solves end to end.
- Define the files, data exports and systems each task runs on, such as ERP, MES and WMS extracts, so the scenario reflects realistic planning conditions.
- Write a working solution for each task, for example a 5-day production plan, and confirm it can be solved in 60 to 75 minutes.
- Build the automated check that proves the result is correct, using tests or scripts that accept valid answers and catch planning errors objectively.
- Document the written rules, constraints and SOPs that decide each case, along with realistic edge cases, data inconsistencies and common planner errors from your process.
- Iterate on tasks based on reviewer feedback, keeping instructions unambiguous, data consistent and the solution and verification reproducible in a command-line environment.
Skills/Experience
- 3+ years of hands-on supply chain or production planning work (not only studying, selling or managing it), or a PhD with active research in the subfield
- Did the work within the last 3 years and can name the systems, tools, data and rules used
- Personally executed a process where each case is decided by written rules such as SOPs, planning rules or constraints
- Worked inside systems of record (ERP, WMS) and with their exports or reports
- Can describe edge cases, data inconsistencies and common planner errors, and how a mistake would be caught
- Technical fluency in Python or another language and the command line, plus files, CSV/JSON, APIs or databases
- Available to start around November 4, 2026
Nice-to-have
- Automated or audited the process with scripts, SQL, RPA or rules engines
- 5+ years in the same process family
- Docker, Git and experience writing tests
- Hands-on experience with SAP or other ERPs and TMS/WMS platforms
Process
- Task-based engagement paid per task, with each task taking about 60 to 75 minutes
- Vetting task instead of a live interview
- Tasks are reviewed by the client before approval
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