Scientific Coding Expert - Material science and Python

Turing is developing one of the most rigorous STEM AI training datasets in the industry through its SciCode project. This initiative focuses on creating high-quality scientific coding tasks that help train and evaluate advanced AI models. As a SciCode Trainer, you will play a vital role in advancing AI research by authoring, implementing, and reviewing complex scientific problems across core STEM disciplines. Your work will directly contribute to the development of frontier AI technologies.

Key Responsibilities

- Author detailed scientific problem specifications, each containing one main problem and at least three logically connected sub-problems that progressively lead to the solution of the main problem.
- Implement verified golden solutions in Python, ensuring comprehensive unit test coverage.
- Design discriminative test cases that effectively distinguish between correct and incorrect model outputs.
- Conduct quality control validation checks on the Turing Central Task Platform (CTP), including Tier 1 structural checks and Tier 2 quality rubrics.
- Iterate on tasks based on QC feedback to meet Pass@K evaluation criteria across multiple large language model judges such as GPT, Gemini, and Nemotron.
- Maintain a high standard of output quality with a low rework rate, aiming for consistent first-time approval at Level 1.
- Participate in regular sync calls for task reviews, feedback sessions, and project standups during overlapping hours with the PST time zone.

Required Qualifications

- Master’s or PhD degree in Material Science or a related STEM field.
- Strong Python programming skills, particularly with experience in scientific computing.
- Proven ability to write rigorous and well-posed scientific problems with clear constraints and expected outputs.
- Exceptional attention to detail to ensure tasks meet strict rubrics for well-posedness, test case discriminativeness, scientific accuracy, and determinism.
- Prior experience in AI data annotation, research, or scientific writing.
- Familiarity with large language model evaluation frameworks or coding benchmarks.
- Experience using scientific libraries such as NumPy, SciPy, SymPy, or other domain-specific scientific tools.
- Demonstrated academic or research experience in a STEM domain, including published research or significant project work.

Preferred Qualifications and Offer Details

- Commitment to a 40-hour workweek with a required 4-hour overlap with Pacific Standard Time (PST).
- Engagement as a contractor or freelancer; this is a non-permanent role without medical benefits or paid leave.

This role offers a unique opportunity to contribute to cutting-edge AI research by leveraging your expertise in scientific problem-solving and programming within a collaborative and innovative environment.