Domain Expert: Chemistry

About Turing: Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality data, evaluations, and reinforcement learning environments that improve model capabilities in coding, reasoning, tool use, and multimodality. In coding, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across Financial Services, Life Sciences, Healthcare, Retail, Automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Role Overview We are hiring US-based academic chemists to support the curation and evaluation of high-quality chemistry data for frontier AI models and research laboratories. This is the Senior Reviewer track of the project. You will review complex chemistry problems and AI-generated solutions, resolve disagreements among reviewers, develop evaluation frameworks, and serve as a subject-matter advisor to client teams assessing AI model outputs. This opportunity is designed for chemistry faculty and experienced researchers interested in applying their academic expertise to rigorous AI evaluation, scientific reasoning, and reliable AI training infrastructure. What You’ll Do Day-to-Day Conduct senior-level reviews of complex or ambiguous chemistry problems and AI-generated solutions. Evaluate responses for scientific accuracy, logical consistency, mathematical correctness, clarity, and adherence to instructions. Verify chemical reactions, mechanisms, structures, calculations, nomenclature, units, assumptions, and conclusions. Identify factual errors, unsupported claims, unsafe recommendations, and invalid scientific reasoning. Facilitate the structured resolution of disagreements among chemistry reviewers. Develop and refine evaluation rubrics, review guidelines, and quality standards. Provide expert advisory support to client teams reviewing AI model performance. Write technically rigorous, publication-quality explanations and reviewer feedback. Provide consultation within relevant chemistry specializations. Document review decisions and scientific consensus clearly and consistently. Follow all project protocols, confidentiality requirements, and quality standards. Required Qualifications PhD in Chemistry, Chemical Sciences, or a closely related discipline from an accredited institution. Faculty appointment at the rank of Assistant Professor or above at an accredited US university or academic institution. At least three years of postdoctoral, faculty, or equivalent professional research experience. Active involvement in chemistry research, teaching, or academic supervision. Strong command of chemical principles, quantitative reasoning, and laboratory practices. Demonstrated publication record in peer-reviewed chemistry or interdisciplinary scientific journals. Experience teaching undergraduate or graduate-level chemistry courses. Strong written communication skills and the ability to explain complex chemical concepts clearly. Ability to evaluate multiple valid approaches and determine whether conclusions are scientifically defensible. Experience reviewing technical papers, research submissions, examination questions, or scientific content. Ability to lead and document expert consensus. Strong attention to detail and disciplined adherence to evaluation protocols. Relevant Areas of Expertise Expertise in one or more of the following areas is preferred: Organic chemistry Inorganic chemistry Physical chemistry Analytical chemistry Biochemistry and chemical biology Medicinal and pharmaceutical chemistry Computational and theoretical chemistry Materials and polymer chemistry Environmental chemistry Electrochemistry Surface and colloid chemistry Spectroscopy and chemical characterization Chemical thermodynamics and kinetics Catalysis and reaction mechanisms Synthetic chemistry Industrial and process chemistry Preferred Qualifications Prior experience with AI evaluation, scientific data annotation, or large language model research. Experience developing scientific rubrics, benchmarks, or assessment frameworks. Familiarity with computational chemistry or scientific tools such as Python, MATLAB, R, ChemDraw, Gaussian, ORCA, or similar platforms. Experience with molecular modeling, simulations, spectroscopy, or scientific data analysis. Prior journal peer-review or editorial experience. Service on research, academic, grant-review, safety, or ethics committees. Experience mentoring graduate students, postdoctoral researchers, or junior faculty. Active participation in the American Chemical Society or another recognized chemistry organization. Familiarity with evaluating AI-generated chemical structures, mechanisms, calculations, and scientific explanations. Performance Expectations Take senior-review responsibility for assigned case batches. Deliver scientifically accurate and defensible evaluations. Maintain consistent quality and reliability across assignments. Clearly document errors, assumptions, calculations, mechanisms, and review decisions. Collaborate constructively with other faculty-level reviewers. Communicate professionally with client teams during advisory sessions. Meet agreed deadlines and project quality requirements. Maintain confidentiality and comply with all project protocols. Conflict-of-Interest Disclosure Applicants must disclose any active equity, advisory, consulting, research-funding, or paid relationships with: Artificial intelligence companies Chemical, pharmaceutical, biotechnology, or materials companies Scientific software or laboratory technology companies Research organizations whose work may appear in project content Other commercial entities relevant to assigned cases Disclosed conflicts will not automatically disqualify an applicant but may be considered when assigning work. Perks of Freelancing with Turing Competitive compensation and flexible remote work. Learn how advanced AI systems are trained and evaluated. Work on cutting-edge projects with leading AI laboratories. Collaborate with researchers and experts from diverse scientific backgrounds. Potential for contract extension based on project needs and performance. Flexible, asynchronous work designed to complement academic responsibilities. Offer Details Engagement type: Independent contractor (1099) Payment model: Pay per task Workload: Approximately 15 hours per week on average, with up to 40 hours per week during peak batches Location: United States Work arrangement: Fully remote and primarily asynchronous, with occasional synchronous calls Duration: Four weeks, with potential extension depending on project requirements Timeline: Subject to project and assignment availability Compensation: Determined based on experience, academic standing, research background, and area of expertise Confidentiality: NDA required before engagement; the detailed scope of work will be shared after NDA execution Evaluation Process Applicants must submit: An updated CV containing faculty appointment and research details Two recent peer-reviewed publications A brief statement of interest of approximately 300 words describing experience with scientific writing, chemistry evaluation, peer review, or AI research Shortlisted applicants will complete: A qualification review One interview round A brief paid calibration assignment before full engagement