About this role
In this hourly, remote contractor role, you will work as a Chemistry Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across chemistry AI training projects. You will review AI-generated chemistry content and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure that all contributors follow the expected quality standards.
You will assess work for scientific accuracy, chemical reasoning, calculation correctness, reaction logic, unit consistency, safety awareness, clarity, formatting, instruction-following, and adherence to project-specific rubrics. You will spot recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently. This role requires strong chemistry expertise, strong English communication skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote technical teams.
This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your chemistry quality leadership will directly help improve the world’s premier AI models by ensuring that chemistry training data is accurate, logically sound, clearly explained, safety-aware, well-documented, and aligned with client expectations.
Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.
Requirements
- Bachelor’s, Master’s, or PhD degree in Chemistry, Chemical Sciences, Biochemistry, Materials Chemistry, Pharmaceutical Chemistry, or a closely related field.
- Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear scientific feedback in English.
- 3+ years of professional experience in chemistry research, laboratory work, teaching, scientific writing, technical review, quality control, formulation, analytical chemistry, or related workflows.
- Strong understanding of core chemistry topics such as general chemistry, organic chemistry, inorganic chemistry, physical chemistry, analytical chemistry, biochemistry, thermodynamics, kinetics, equilibrium, stoichiometry, spectroscopy, and reaction mechanisms.
- Ability to evaluate chemistry content against detailed rubrics and identify issues such as incorrect assumptions, flawed calculations, invalid reaction logic, missing units, unsafe recommendations, hallucinated facts, incomplete explanations, or misleading scientific claims.
- Familiarity with common chemistry tools or workflows such as laboratory documentation, analytical methods, spectroscopy interpretation, chromatography, titration analysis, molecular structure interpretation, safety data sheets, chemical nomenclature, or scientific literature review is preferred.
- Experience leading or supporting remote teams of trainers, annotators, reviewers, researchers, technical writers, educators, or QAs is strongly preferred.
- Comfortable working in fast-moving remote environments using tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems.
- Highly detail-oriented and organized, with the ability to maintain style guides, FAQs, trackers, onboarding materials, honeypots, calibration tasks, and other quality documentation.
- Experience with AI training, data annotation, large language models, prompt/response evaluation, scientific content QA, or rubric-based LLM evaluation is a strong plus.
Responsibilities
- Quality monitoring: Spot-check chemistry items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
- Scientific review: Evaluate AI-generated chemistry explanations, calculations, reaction mechanisms, laboratory reasoning, nomenclature, diagrams/descriptions, and problem-solving steps for correctness and clarity.
- Trainer and QA communication: Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and chemistry-specific review standards.
- Question handling: Respond to trainer/QA questions clearly and promptly, especially around chemical reasoning, units, formulas, reactions, mechanisms, safety concerns, terminology, and rubric interpretation.
- Trainer/QA activation management: DM contributors who are inactive or not working, encourage activation, track follow-ups, and flag availability issues when needed.
- Documentation: Create and maintain chemistry project documentation, including style guides, trackers, FAQs, quality notes, examples, honeypots, calibration tasks, and onboarding materials.
- Onboarding and training: Schedule and run onboarding/training calls with trainers and QAs to explain project expectations, workflows, rubrics, quality standards, and chemistry-specific review requirements.
- Quality alignment: Ensure all trainers and QAs apply chemistry guidelines consistently and understand updates as projects evolve.
- Risk and safety review: Flag unsafe, misleading, or overconfident chemistry recommendations, especially where chemical handling, reactions, lab procedures, hazardous substances, dosing, environmental impact, or health/safety may be affected.
- Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for chemistry AI training projects.
Skills
- Reaction Mechanisms
- LLM Evaluation
- Chemistry QA
- Chemical Reasoning
- Chemistry
- AI Training
- Documentation
- Stoichiometry
- Trainer Feedback
- Scientific Review
Additional skills
- Quality Assurance (QA)
- Scientific Writing
- Technical Editing
- Analytical Chemistry
- Organic Chemistry
- Inorganic Chemistry
- Physical Chemistry
- Biochemistry
- Thermodynamics
- Chemical Kinetics
- Chemical Equilibrium
- Stoichiometry
- Reaction Mechanisms
- Spectroscopy
- Chromatography (HPLC/GC)
- Titration
- Mass Spectrometry
- NMR Spectroscopy
- UV-Vis Spectroscopy
- Chemical Safety
- Safety Data Sheets (SDS)
- Chemical Nomenclature (IUPAC)
- Laboratory Documentation
- Method Validation
- Data Annotation
- AI Training Data QA
- Large Language Models (LLMs)
- Rubric-Based Evaluation
- Prompt Evaluation
- Google Sheets
