Materials Science Analyst

Overview

About Gramian Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs. Role Overview We are looking for a materials science professional to contribute to an advanced AI research project focused on building rigorous STEM coding datasets. The role combines materials science expertise, Python-based scientific computing, and AI evaluation , with a focus on creating well-structured scientific problems, implementing verified solutions, and developing tests that accurately distinguish correct from incorrect model outputs. Responsibilities Design scientific coding tasks with one main problem and at least 3 logically connected sub-problems. Implement verified Python solutions with complete unit test coverage. Create discriminative test cases that distinguish correct from incorrect AI-generated outputs. Perform quality control checks using the Central Task Platform (CTP), including Tier 1 structure checks and Tier 2 quality rubrics. Revise tasks and solutions based on QC feedback. Optimize tasks against Pass@K evaluation criteria across multiple LLM judges, including GPT, Gemini, and Nemotron. Validate scientific correctness, determinism, well-posedness, and expected outputs. Maintain a low rework rate and high first-submission quality. Participate in project reviews, feedback sessions, and standups during required overlap hours. CONTRACT: Freelance / Contractor COMMITMENT: Full-time commitment; overlap requirements to be confirmed LOCATIONS: Remote; eligible locations to be confirmed PROCESS: Not specified Requirements Design scientific coding tasks with one main problem and at least 3 logically connected sub-problems. Implement verified Python solutions with complete unit test coverage. Create discriminative test cases that distinguish correct from incorrect AI-generated outputs. Perform quality control checks using the Central Task Platform (CTP), including Tier 1 structure checks and Tier 2 quality rubrics. Revise tasks and solutions based on QC feedback. Optimize tasks against Pass@K evaluation criteria across multiple LLM judges, including GPT, Gemini, and Nemotron. Validate scientific correctness, determinism, well-posedness, and expected outputs. Maintain a low rework rate and high first-submission quality. Participate in project reviews, feedback sessions, and standups during required overlap hours.