Scientific Coding: Redefining Mathematics and Python Evaluation with Turing

The boundary between theoretical mathematics and artificial intelligence is dissolving. As language models scale to handle advanced computational reasoning, the tech industry faces an acute shortage of rigorous, high-quality STEM datasets. General coding benchmarks are no longer sufficient to evaluate complex mathematical logic, multi-step algorithmic proofing, and scientific computation.

To bridge this gap, Turing is building one of the most rigorous STEM AI training datasets in the industry through the pioneering SciCode project. This initiative brings together elite mathematicians, data scientists, and python engineers to create sophisticated coding tasks that evaluate and elevate frontier AI models.

About the Role: SciCode Trainer

As a Scientific Coding Trainer specializing in Mathematics and Python, your mission is to author, implement, and review complex scientific problems across core STEM fields. You will operate as a principal architect of evaluation scenarios, translating high-level mathematical concepts into structured, deterministic code challenges for AI models.

Your work directly impacts how accurately large language models diagnose complex mathematical proofs, execute numerical simulations, and handle advanced scientific computation frameworks.

A Day in the Life: Daily Responsibilities

• Problem Specification Authoring: Craft rigorous scientific problem sets consisting of one main problem and a minimum of three logically progressive sub-problems.
• Python Implementation: Develop verified golden solutions in Python, ensuring complete unit test coverage and robust error handling.
• Discriminative Test Design: Engineer test cases that sharply differentiate correct model reasoning from flawed outputs.
• Quality Control Validation: Execute Tier 1 structure checks and Tier 2 quality rubrics on the Turing Central Task Platform (CTP).
• Model Alignment Iteration: Refine tasks based on QC feedback to satisfy strict Pass@K evaluation criteria across multiple LLM judges (GPT, Gemini, Nemotron).
• Collaboration & Syncs: Participate in review cycles, feedback sessions, and project standups during required overlap windows.

The Interview Process & Onboarding Roadmap

Securing an elite engineering assignment with Turing involves a structured evaluation pipeline designed to verify technical depth and operational reliability:

1. Credential Screening: Verification of your academic background in mathematics and programming proficiency.
2. Technical Assessment: Practical evaluation testing your ability to construct scientific coding tasks and unit tests.
3. Onboarding & Initial Integration: Completing orientation and successfully pushing through your first milestones.

Crucial Milestone for Stability: Successfully completing your onboarding and achieving at least 10 hours of logged work is critical. This initial milestone ensures long-term assignment stability, grants access to advanced project tiers, and establishes your reputation within the network.

Perks of Partnering with Turing

Contracting with Turing offers unmatched flexibility and professional growth for elite global talent. You gain direct exposure to cutting-edge AI research labs, collaborate with brilliant international peers, and enjoy fully remote autonomy while working on transformative technologies.

Note: This engagement requires a 4-hour daily overlap with PST hours and a full-time commitment of 40 hours per week across approved regions (including Brazil, Colombia, India, Turkey, Vietnam, and others).