Job Category: Coding & STEM

In this role, you will be working on projects to help fine-tune large language models (like ChatGPT) using your strong analytical and English comprehension skills The ideal candidate should have a strong foundation in Chemistry, comparable to the level required for PhD-level programs. You should be able to break down complex chemistry problems into simple, clear explanations and work efficiently. The projects will also help you learn how to leverage AI to be a better analyst. Main tasks listed: You would spend time creating and solving a variety of advanced chemistry problems, including those at the postgraduate level, and creating detailed explanations. You’ll regularly work with a mix of chemistry content—combining text with images, chemical equations, diagrams, and visual representations. Here are a few examples of the kinds of tasks you might handle day to day:; Analyze and explain the steps in a complex organic reaction mechanism, providing both textual explanations and annotated chemical structures; Solve a problem involving chemical equilibrium, accompanied by visual representations of reaction progress and energy diagrams, and explain the process using both text and visual aids; Address challenging problems involving stoichiometry, thermodynamics, organic chemistry, and electrochemistry, typical of Engineering Entrance Exam or Engineering College level questions, utilising a combination of textual, visual, and diagrammatic explanations.

In this role, you will be working on projects to help fine-tune large language models (like ChatGPT) using your strong analytical and english comprehension skills The ideal candidate should have a solid foundation in Biology, particularly at the level expected in engineering entrance exams, as well as graduate or PhD-level programs. You should be able to break down complex biological concepts into simple, clear explanations and work efficiently. Main tasks listed: Design and solve challenging Biology problems to probe the limitations of large language models; Develop high-quality, step-by-step solutions with clear and rigorous reasoning; Collaborate with LLM researchers to align problem types with evaluation goals, especially in areas where models typically struggle (e.g., conceptual abstraction, multi-step reasoning, and data interpretation); Contribute to defining new evaluation benchmarks based on Biology curricula spanning early undergraduate to PhD-level topics.

Remote contract work for hardware engineers on AI training projects: create rigorous, self-contained hardware engineering problems based on realistic scenarios and develop reference solutions with calculations, simulations or scripts (for example Python, MATLAB, Simulink, SPICE or C/C++).

Remote contract work for aerodynamics specialists on an AI training project: create, solve and validate CFD and aerodynamic tasks through command-line workflows, such as preparing domains and meshes, setting boundary conditions, running simulations, computing lift and drag and checking that results are physically valid. Python is used to automate the workflow.

Remote contract work for mechanical engineers on an AI training project: create, solve and validate engineering tasks across structural, thermal, mechanical and multiphysics domains, such as building computational models, meshes, materials and boundary conditions, running simulations and checking results against physical and design constraints. The listing describes the role as very coding-heavy.

Remote contract work for STEM experts on an AI training project: solve, review and validate challenging problems in your field, use Python for calculations or simulations, and check whether AI-generated answers are technically correct. Fields include chemistry, biology, mathematics, physics, earth and environmental science and robotics. The listing describes the role as very coding-heavy.

Remote contract work for biomedical signal processing and machine learning specialists: create rigorous, self-contained problems with physiological signals and time-series data (preprocessing, filtering, feature extraction, classification or forecasting) and write reference solutions in Python, MATLAB or similar. No prior AI-training experience is required.

Remote contract work for chemists: design and critique chemistry and computational chemistry problems and reasoning used to benchmark AI models, review experimental protocols and simulation settings, and analyse chemical, spectroscopic and simulation data. Areas include organic and inorganic chemistry, physical and theoretical chemistry, analytical chemistry, computational chemistry and molecular dynamics, cheminformatics and materials science.

Remote contract work for biologists and bioinformaticians: design, evaluate and critique biology and bioinformatics workflows used to benchmark AI models, review analysis pipelines for genomic, transcriptomic and proteomic data, and analyse datasets such as NGS, RNA-seq, single-cell and protein structure data.

Remote contract work for life scientists: write expert-level scientific problems with verifiable solutions, review AI model answers for scientific accuracy and depth of reasoning, and build rubrics and reference answers grounded in the literature. Areas include genomics, single-cell and spatial omics, proteomics, microbiome, structural biology, drug discovery and translational research.