Materials Engineer & Python AI Challenge Architect

Mindrift connects specialists with project‑based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment. What this opportunity involves You design computational material science problems to challenge a frontier AI model. The problem must have an answer verifiable by code, and it has to require a specialized tool like ObsPy, instaseis, pyrocko, MITgcm, flopy/MODFLOW, or others. Generic data wrangling around synthesised toy data won't cut it. Each problem runs inside a sealed Linux container with the tool pre‑installed and a programmatic judge that grades the model's answer. As an expert author, you: Pick an anchor tool and design a problem that hinges on its waveform‑processing kernels, geophysical inversion routines, sub‑surface flow solvers, or community‑validated data pipelines. Write a Python reference solution, supply input files and model or domain definitions where needed. Decide the numerical answer and how close the model needs to get — with a domain‑appropriate tolerance — to count as right. Test the problem against the model in batches of parallel attempts, tuning the problem difficulty until the agent only succeeds in a small number of attempts. Once you are happy with the task, and it scores within range, the task goes to a senior reviewer in your subfield. They will provide feedback to ensure task quality is high. Calibration requires patience. You're tuning the problem against batches of parallel runs of the agent, aiming for a pass rate in the 10–30% band. Reaching that means rewriting waveform scenarios, tightening inversion parameters and solver tolerances, and watching how the agents act. You'll learn how these agents cut corners, where a simulation stalls, where a flow or inversion model converges. This time compounds in two directions. You come out of each task with deeper command of the anchor tool itself, and also get a hands‑on working intuition for how a frontier model navigates complex seismic, oceanographic, and sub‑surface flow problems. What we look for Degree in Material Science or related field; 2+ years of research, applied, or teaching experience; Python proficiency for writing reference solutions; Fluency with — or strong willingness to independently learn — at least one scriptable package: ObsPy, instaseis, pyrocko, MITgcm, xmitgcm, flopy / MODFLOW, or GeoPandas; Ability to design problems that genuinely require a specialized solver; Strong written English (C1+). No prior experience with the listed tools? You're still welcome to apply — as long as you're ready to get up to speed on your own and hit the ground running. Project time expectations For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. Compensation On this project, contributors can earn up to $45 per hour equivalent , depending on your level and pace of contribution. Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements. #J-18808-Ljbffr AI Chopping Block, Inc.