Biology Python Scientific Coding Contractor (Remote)
Listing checked September 8, 2026 · pay as published by Turing
Overview
Turing, in partnership with NVIDIA, is assembling a rigorous STEM AI training dataset through the SciCode project. This initiative focuses on creating high-quality scientific coding tasks that help train and evaluate frontier AI models. As a SciCode Trainer, you will craft, implement, and review intricate scientific problems in biology, directly contributing to AI research that pushes the boundaries of what models can solve. Expect to work within a structured environment that demands precision, scientific accuracy, and a deep understanding of both biology and Python.
What You'll Do8
- 1Draft detailed scientific problem specifications, each built around one main problem and a minimum of three logically connected sub-problems that build toward a complete solution.
- 2Develop and validate correct solutions in Python, ensuring they pass rigorous unit tests and meet all specified constraints.
- 3Design test cases that are discriminative, meaning they clearly separate correct model outputs from incorrect ones, testing edge cases and subtle scientific reasoning.
- 4Run quality control checks on the Turing Central Task Platform (CTP), including Tier 1 structure checks and Tier 2 quality rubrics, to ensure tasks meet strict standards.
- 5Revise tasks based on QC feedback to achieve Pass@K evaluation criteria, which involves scoring well across multiple LLM judges like GPT, Gemini, and Nemotron.
- 6Maintain high output quality, aiming for consistent L1 approval on the first submission and keeping rework rates low.
- 7Join scheduled sync calls for review, feedback, and project standups, which require a 4-hour overlap with Pacific Standard Time.
- 8Participate in a collaborative environment where you receive feedback from fellow scientific experts and contribute to improving task quality.
Requirements7
- 1Hold a Master's or PhD in Biology.
- 2Demonstrate strong Python programming skills, particularly in scientific computing, and be comfortable using libraries such as NumPy, SciPy, SymPy, or other domain-specific tools.
- 3Show ability to write rigorous, well-posed scientific problems with clear constraints and expected outputs, ensuring scientific correctness and determinism.
- 4Possess a keen attention to detail, as tasks must adhere to strict rubrics for well-posedness, test case discriminativeness, and scientific accuracy.
- 5Have prior experience in AI data annotation, research, or scientific writing; familiarity with LLM evaluation frameworks or coding benchmarks is a plus.
- 6Provide evidence of published research or academic projects in a STEM field.
- 7Commit to a full-time schedule of 40 hours per week and be available for overlap with PST for the duration of the 8-week contract.
Who Should Apply
This role suits a biologist with strong Python skills who enjoys the challenge of turning complex scientific concepts into structured coding problems. If you have a graduate degree and practical experience writing scientific code, you will find this work both demanding and rewarding. It is less suitable for those who prefer open-ended research or lack the discipline for rigorous quality control. Common reasons candidates fall short: weak Python fundamentals, inability to write unambiguous scientific problems, or a history of rushed submissions that require extensive rework.
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Highlight specific examples of how you have designed scientific coding problems or test cases in the past. If possible, quantify the quality of your previous work, such as acceptance rates or feedback scores, to demonstrate your ability to meet strict rubrics on the first try.
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