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AI training & evaluation

Scientific Coding - Biology and Python

Turing

Turing is building one of the most rigorous STEM AI training datasets in the industry in partnership with NVIDIA.

Work arrangement & location
Remote
Hours
40 hours per week
  • Science
  • Software Engineering
  • Biology
  • Software development
  • biology
  • python
Apply on Turing

Employer description and requirements

Role Overview:

Turing is building one of the most rigorous STEM AI training datasets in the industry in partnership with NVIDIA. 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 Biology or similar fields.

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

Offer Details:

Commitments Required : Overlap of 4 hours with PST and 40 hrs/week

Engagement type : Contractor assignment/freelancer (no medical/paid leave)

Duration of contract : 8 weeks

Location : Bangladesh, Brazil, Colombia, Egypt, Ghana, India, Pakistan, Indonesia, Kenya, Nigeria, Turkey, Vietnam