Chemistry Expert

About Turing

Based in San Francisco, California, Turing is the world’s leading research accelerator for frontier AI labs and a trusted partner for global enterprises deploying advanced AI systems. Turing supports customers in two ways: first, by accelerating frontier research with high-quality data, advanced training pipelines, plus top AI researchers who specialize in coding, reasoning, STEM, multilinguality, multimodality, and agents; and second, by applying that expertise to help enterprises transform AI from proof of concept into proprietary intelligence with systems that perform reliably, deliver measurable impact, and drive lasting results on the P&LJob Overview We are seeking professionals with strong scientific reasoning and quantitative analysis skills to create high-quality reasoning datasets for Large Language Model training. This role focuses on designing scientific discovery tasks in which models analyze experimental or simulated data, identify patterns, infer formulas or laws, and apply them to new cases. Ideal candidates have a solid understanding of the scientific method, experimental design, mathematical modeling, and data interpretation across fields such as physics, chemistry, and biology. They should be able to design clear, challenging tasks that test whether a model can move from observation to explanation in a precise and verifiable way.

Key Responsibilities

• Design scientific scenarios using experimental data, observational records, or simulated systems, including fictional but internally consistent systems.
• Author multi-step reasoning tasks that require models to analyze data, identify relationships, infer governing rules, estimate parameters, and predict outcomes.
• Develop problem types such as law induction from data, parameter recovery, model selection, predictive reasoning, and impossible-scenario detection.
• Create clear context blocks, prompts, expected answers or reference solutions, and scoring rubrics for each task.
• Anticipate edge cases such as noisy data, boundary conditions, impossible states, missing assumptions, and unit inconsistencies.
• Collaborate with reviewers and LLM engineers to improve task clarity, scientific accuracy, and reproducibility.
• Maintain high quality standards across all tasks, with strong attention to logic, precision, and internal consistency.

Qualifications

• 3+ years of experience in a scientific, research, or analytical role such as physics, chemistry, scientific computing, or data analysis.
• Strong background in scientific reasoning, quantitative modeling, and data interpretation.
• Familiarity with experimental design, mathematical modeling, parameter estimation, dimensional analysis, or simulation-based reasoning.
• Proven ability to design structured reasoning problems and explain them clearly in writing.
• Strong attention to detail, especially around assumptions, ambiguity control, and consistency between data, solution, and rubric.
• Experience working with or evaluating Large Language Models is a plus. A degree in Physics, Chemistry, Biology, or a related science discipline is preferred.

Deliverables

• A structured collection of reasoning tasks covering law discovery from data, inference from observational or simulated systems, parameter estimation, prediction under new conditions, and scientific consistency checks.
• Each task should include: A formal context block with assumptions, variables, definitions, and observations A prompt asking the model to infer, compute, justify, predict, or explain An expected output with a deterministic final answer and reference solution A rubric for evaluating correctness, reasoning quality, and completeness Final tasks must be self-contained, reproducible, and aligned with model reasoning evaluation goals.

Offer Details

• Commitments Required: at least 4 hours per day and minimum 40 hours per week with 4 hours of overlap with PST.
• Engagement type: Contractor
• Engagement Length: 5 weeks Evaluation Process -
• Shortlisted candidates will be sent a Job Interest Form.
• Shortlisted candidates will be contacted to discuss the pre‑onboarding requirements