Scientific Coding Expert - Physics and Python

Turing is developing one of the most comprehensive 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 advancing AI research by authoring, implementing, and reviewing complex scientific problems across core STEM disciplines, particularly physics. This position offers an opportunity to contribute directly to cutting-edge AI model development by ensuring the quality and rigor of scientific coding challenges.

Key Responsibilities:
- Write detailed scientific problem specifications that include one main problem and at least three logically connected sub-problems, progressively building toward the solution of 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 (QC) validation checks 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 consistently high-quality output with a low rework rate, aiming for first-submission approval at Level 1 standards.
- Participate in regular sync calls for reviews, feedback sessions, and project standups during overlapping hours with the team.

Required Qualifications:
- Master’s or PhD degree in Physics, demonstrating strong subject matter expertise.
- Proficiency in Python programming, especially with experience in scientific computing.
- Ability to craft rigorous, well-defined scientific problems with clear constraints and expected outputs.
- Exceptional attention to detail to ensure tasks meet strict rubrics related to problem clarity, test case discriminativeness, scientific accuracy, and determinism.
- Prior experience in AI data annotation, scientific 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 computational tools.
- Evidence of published research or academic projects in a STEM field is highly desirable.

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, with no medical benefits or paid leave provided.
- The contract duration is set for 8 weeks, offering a focused, project-based assignment.

This role is ideal for candidates passionate about scientific problem-solving and AI, looking to apply their expertise in physics and programming to help shape the future of AI training datasets.