Scientific Coding Expert - Physics and Python

Turing is developing one of the most advanced STEM AI training datasets through its SciCode project, which focuses on creating high-quality scientific coding tasks. These tasks are designed to train and evaluate state-of-the-art 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, particularly physics. This position requires a strong foundation in scientific problem-solving and programming, contributing directly to the quality and rigor of AI training data.

Key Responsibilities:
- Write detailed scientific problem specifications that include one main problem supported by at least three logically connected sub-problems, which 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 model outputs.
- Conduct quality control (QC) validation checks on the Turing Central Task Platform (CTP), including Tier 1 structural checks and Tier 2 quality rubrics.
- Iterate on task development based on QC feedback to meet Pass@K evaluation criteria across multiple large language model (LLM) judges such as GPT, Gemini, and Nemotron.
- Maintain consistently high output quality with a low rework rate, aiming for first-time approval at Level 1 (L1).
- Participate actively in synchronization calls for reviews, feedback sessions, and project standups during overlapping working hours.

Required Qualifications:
- Master’s or PhD degree in Physics.
- Strong Python programming skills, particularly with experience in scientific computing.
- Proven ability to write rigorous, well-posed scientific problems that include clear constraints and expected outputs.
- Exceptional attention to detail to ensure tasks meet strict rubrics related to well-posedness, test case discriminativeness, scientific correctness, and determinism.
- Prior experience in AI data annotation, scientific 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, ideally with published work.

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 or freelancer basis, without medical benefits or paid leave.
- The contract duration is 8 weeks.

This role offers a unique opportunity to contribute to cutting-edge AI research by shaping the datasets that train future scientific AI models. The position demands a blend of scientific expertise, programming proficiency, and a meticulous approach to problem design and validation. If you are passionate about STEM and AI, and enjoy working in a dynamic, research-driven environment, this role provides a challenging and rewarding experience.