Expertise: science_expert

We are seeking a highly qualified TV Shows & Movies Domain Expert to evaluate and improve Large Language Models (LLMs). You will design challenging prompts, assess factual accuracy and reasoning, and identify knowledge gaps within film, television, streaming, actors, directors, genres, awards, franchises, and entertainment history. Main tasks listed: Create advanced prompts covering film, television, streaming, actors, directors, genres, awards, franchises, and entertainment history; Evaluate AI-generated responses for factual accuracy, reasoning quality, completeness, and nuance; Identify hallucinations, logical inconsistencies, outdated information, and edge cases; Develop benchmark datasets and adversarial test cases.

We are building an evaluation benchmark for frontier AI browsing agents. Your job is to design research problems that a state-of-the-art AI cannot solve, even with full web access and multiple attempts. This is not a subject matter expert role, nor is it a content-writing role. It is investigative research. You will start from a verifiable fact, work backwards to construct a question that makes that fact extremely hard to locate, and then prove your work with a complete, auditable evidence trail.

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 STEM, 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 STEM concepts into simple, clear explanations and work efficiently. Main tasks listed: Design and solve challenging STEMs problems to probe the limitations of large language models; Create clear, high-quality, step-by-step solutions with well-articulated reasoning; Collaborate with LLM researchers to align problems with evaluation goals, especially in areas where models typically struggle (e.g., abstraction, multi-step reasoning, symbolic manipulation); Help define new evaluation benchmarks based on Physics curricula spanning early undergraduate to PhD-level topics.

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 Physics, 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 Physics concepts into simple, clear explanations and work efficiently. Main tasks listed: Design and solve challenging Physics problems to probe the limitations of large language models; Create clear, high-quality, step-by-step solutions with well-articulated reasoning; Collaborate with LLM researchers to align problems with evaluation goals, especially in areas where models typically struggle (e.g., abstraction, multi-step reasoning, symbolic manipulation); Help define new evaluation benchmarks based on Physics curricula spanning early undergraduate to PhD-level topics.

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 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.