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FREELANCE Scientific Coding Expert - Chemistry

FREELANCE Scientific Coding Expert - Chemistry

willware technologies
Fresher
Not Disclosed
Early Applicant
  • Posted a month ago
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Job Description

COMPANY: WILLWARE TECHNOLOGIES

ROLE: FREELANCE - Scientific Coding Expert - Chemistry

WORK MODE: REMOTE

Job 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 Pass@K 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 Chemistry. 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

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