Scientific Coding Expert - Material science

WILLWARE TECHNOLOGIES PRIVATE LIMITED · India

COMPANY: WILLWARE TECHNOLOGIES

ROLE: FREELANCE -  Scientific Coding Expert - Material science

WORK MODE: REMOTE

Offer Details:

• Commitments Required : Overlap of 4 hours with PST and 40 hrs/week 
  Talent Rate: $180/task upon approval
• Engagement type : Contractor assignment/freelancer (no medical/paid leave)
• Duration of contract : 8 weeks 

Role Overview

Turing is building one of the most rigorous STEM AI training datasets in the industry. The SciCode project involves creating high-quality scientific coding tasks that are used to train and evaluate frontier AI models. As a SciCode Trainer, you will be directly contributing to cutting-edge AI research by authoring, implementing, and reviewing complex scientific problems across core STEM disciplines.

Responsibilities:

• Write scientific problem specifications consisting of one main problem and a minimum of 3 sub-problems, all logically connected and progressively building toward the main problem solution Implement verified golden solutions in Python with complete unit test coverage
• Design discriminative test cases that clearly differentiate correct from incorrect model outputs Run QC validation checks on the Turing Central Task Platform (CTP) including Tier 1 structure checks and Tier 2 quality rubrics Iterate on tasks based on QC feedback to meet evaluation criteria across multiple LLM judges (GPT, Gemini, Nemotron) 
• Maintain high output quality with a low rework rate, targeting consistent L1 approval on first submission 
• Participate in sync calls for reviews, feedback sessions, and project standups during overlap hours

Required Qualifications:

•  Master's or PhD in Material science or related field. Strong Python programming skills with experience in scientific computing
• Ability to write rigorous, well-posed scientific problems with clear constraints and expected outputs 
• Attention to detail - tasks must meet strict rubrics for well-posedness, test case discriminativeness, scientific correctness, and determinism
•  Prior experience in AI data annotation, research, or scientific writing Familiarity with LLM evaluation frameworks or coding benchmarks Experience with libraries such as NumPy, SciPy, SymPy, or domain-specific scientific tools . Published research or academic project experience in a STEM domain Quality Standards