Scientific Coding: Material science and Python

What you'll do

Materials science is the least common discipline in this SciCode family, and the work is the same shape as the others: author a main problem with at least three linked sub-problems, implement a verified golden solution in Python with complete unit tests, and design test cases that clearly separate right from wrong model answers.

Everything is checked on Turing's Central Task Platform (structure checks, then quality rubrics) and iterated until it meets Pass@K criteria across LLM judges (GPT, Gemini, Nemotron). Expect sync calls for reviews and standups during overlap hours.

Who qualifies

• Master's or PhD in Material science or a related field, so adjacent fields such as chemistry or physics may fit
• Strong Python and scientific computing
• Ability to write well-posed problems with clear constraints and expected outputs
• Rigour on well-posedness, discriminative tests, scientific correctness and determinism
• Prior AI data annotation, research or scientific writing
• Familiarity with LLM evaluation frameworks or coding benchmarks, and NumPy, SciPy, SymPy or domain tools
• Published research or academic project work in a STEM domain

What it pays

The ad does not publish a rate. Turing sets rates per project, see Turing.

Worth knowing

Good:

• The wording "or related field" gives more room than the physics version
• Few contributors in a niche discipline

Less good:

• 40 hours a week and 4 hours of Pacific-time overlap for 8 weeks
• Contractor assignment, no medical or paid leave
• Limited to Bangladesh, Brazil, Colombia, Egypt, Ghana, India, Pakistan, Indonesia, Kenya, Nigeria, Turkey and Vietnam
• Turing's skill tag for the role points to materials and applied chemistry

About this listing

Posted on Turing's job board and read on 18 October 2026. Terms are Turing's; commentary is ours. See Turing.