Scientific Coding Expert - Mathematics and Python

Turing is developing one of the most rigorous STEM AI training datasets through its SciCode project, which focuses on creating high-quality scientific coding tasks to train and evaluate advanced AI models. As a SciCode Trainer, you will play a vital role in cutting-edge AI research by authoring, implementing, and reviewing complex scientific problems across core STEM disciplines. This position offers the opportunity to contribute directly to frontier AI development by ensuring the quality and precision of scientific coding challenges.

Key Responsibilities

- Write detailed scientific problem specifications that include one main problem and at least three logically connected sub-problems, designed to progressively build toward solving the main problem.
- Implement verified golden solutions in Python, ensuring complete unit test coverage for accuracy and reliability.
- Design discriminative test cases that effectively distinguish between correct and incorrect outputs generated by AI models.
- Conduct quality control validation on the Turing Central Task Platform (CTP), including Tier 1 structural checks and Tier 2 quality rubric assessments.
- Iterate on tasks based on QC feedback to meet Pass@K evaluation criteria across multiple large language model (LLM) 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 (L1) on submissions.
- Participate in regular sync calls for task reviews, feedback sessions, and project standups during designated overlap hours.

Required Qualifications

- Master’s degree or PhD in Mathematics.
- Strong proficiency in Python programming, particularly with experience in scientific computing.
- Ability to craft rigorous, well-posed scientific problems with clearly defined 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 LLM evaluation frameworks or coding benchmarks.
- Experience using scientific libraries such as NumPy, SciPy, SymPy, or other domain-specific scientific tools.
- Demonstrated research or academic project experience in a STEM field.

Preferred Qualifications and Offer Details

- Commitment to a 40-hour workweek with a required 4-hour overlap with Pacific Standard Time (PST).
- Engagement is on a contractor/freelancer basis, without medical benefits or paid leave.
- Opportunity to work on a pioneering AI training dataset that directly impacts the development of frontier AI models.

This role is ideal for candidates passionate about scientific problem-solving and AI research, who thrive in a detail-oriented environment and are eager to contribute to innovative AI training efforts.