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We are seeking Life Science experts to contribute advanced scientific expertise to data curation and evaluation for frontier AI models and labs. This senior reviewer role focuses on reviewing complex life science problems, developing evaluation frameworks, and advising client teams assessing AI-generated scientific outputs.
We are seeking experienced in Biology help evaluate and enhance large language model (LLM) outputs related to nutrition, dietary habits, and healthy eating. You’ll assess AI-generated content for scientific accuracy, safety, and relevance — contributing to the development of responsible AI in nutrition and wellness. Selected talents will label and evaluate data related to food intake, dietary patterns, and nutrition information to support AI models focused on nutrition tracking, coaching, or research. Main tasks listed: Annotate food items, portion sizes, meal composition, and nutrient content from text, images, or logs; Classify dietary patterns (e.g., macronutrient balance, meal timing, dietary restrictions/preferences); Verify accuracy of nutritional data against trusted references (e.g., food composition databases); Identify and flag mislabeled, inconsistent, or culturally ambiguous food entries.
In this role, you will work on cutting-edge initiatives to fine-tune large language models by leveraging your deep analytical skills and mathematics expertise. The ideal candidate possesses a strong foundation in Mathematics —spanning advanced engineering entrance levels through graduate and PhD-level concepts—and the ability to break down complex phenomena into clear, step-by-step reasoning. Main tasks listed: In this role, you will help set the benchmark for AI capabilities in advanced physical sciences. Your day-to-day responsibilities will include:; Problem Creation & Solutioning: Designing and solving challenging physics problems that test the boundaries of large language models; Authoring Gold-Standard Data: Creating clear, high-quality, step-by-step solutions with detailed and articulated reasoning; Research Collaboration: Working with LLM researchers to align task designs with evaluation goals, targeting key areas where models struggle (e.g., symbolic manipulation, abstraction, multi-step reasoning).
Commitments Required: At least 6 hours per day and a minimum of 40 hours per week, with a 4-hour overlap with PST. Employment Type: Contractor assignment; no medical or paid leave. Main tasks listed: Design challenging, realistic agentic scientific workflows across the life sciences; Create tasks requiring multiple computational steps rather than single-script or simple question-and-answer solutions; Develop realistic input files, scientific datasets, instructions, constraints, and expected deliverables; Build tasks requiring agents to inspect data, select appropriate methods, execute analyses, troubleshoot problems, and synthesize results.
We are seeking Bioinformatics and Computational Genomics experts to design challenging, agentic genomics tasks and reference solutions for GeneBench Pro. You will create realistic computational biology problems that require an AI agent to interpret biological data, write and execute code, work across scientific files, and produce objectively verifiable outputs. Main tasks listed: • Design novel, challenging tasks in computational genomics and bioinformatics; • Design tasks that test higher-order scientific judgment, including handling ambiguous or messy data, identifying artifacts, selecting appropriate analytical approaches, revising assumptions based on intermediate results, and determining when conclusions are decision-ready; • Build tasks involving realistic scientific data such as FASTA/FASTQ, VCF, BAM/SAM, BED, TSV/CSV, sequence annotations, expression data, and germline or somatic variant data; • Develop tasks requiring multi-step analysis using Python, command-line tools, or established bioinformatics libraries.
We are seeking Chemistry experts to contribute advanced scientific expertise to task design, data curation, and evaluation for frontier AI models and labs. This role focuses on developing rigorous chemistry tasks, reviewing complex scientific outputs, and advising client teams assessing AI-generated solutions.
We are seeking Life Sciences Subject Matter Experts to develop advanced scientific reasoning tasks for LifeSciBench. Unlike coding-focused benchmarks, this role emphasizes deep biological knowledge, scientific interpretation, and rigorous reasoning. You will create challenging questions based on realistic scientific scenarios, experimental results, biological data, figures, literature-style evidence, and research problems. This is a non-code benchmark: tasks are not executed, and no programming is required. Main tasks listed: Design novel, graduate-level life sciences problems grounded in realistic research contexts; Develop clear questions and rigorous reference answers with defensible scientific reasoning; Interpret experiments, datasets, figures, sequences, structures, and other biological evidence; Validate biological claims, calculations, assumptions, and conclusions.
We are seeking Life Sciences Subject Matter Experts to design advanced scientific-reasoning tasks for LifeSciBench. This is a non-coding role focused on biological knowledge, experimental interpretation, data analysis, and rigorous scientific reasoning. You will create challenging, objectively defensible problems based on realistic research scenarios, experimental results, biological data, figures, sequences, structures, and literature-style evidence. Main tasks listed: Design novel, graduate-level life sciences problems grounded in realistic research contexts; Develop clear questions and rigorous reference answers with defensible scientific reasoning; Interpret experiments, datasets, figures, sequences, structures, and other biological evidence; Validate biological claims, calculations, assumptions, and conclusions.
We are seeking Earth Sciences experts to develop realistic, terminal-based scientific tasks for Terminal Bench Science. You will create computational workflows involving environmental data, climate systems, atmospheric processes, geophysics, oceanography, geology, and related fields. You will design tasks that require AI agents to inspect scientific datasets, process geospatial or time-series data, run models, troubleshoot pipelines, and generate objectively verifiable scientific outputs. Main tasks listed: Translate authentic Earth-science workflows into self-contained terminal benchmark tasks; Prepare geospatial, climate, atmospheric, geological, hydrological, or oceanographic datasets; Build reproducible computational environments with appropriate scientific libraries and command-line tools; Create expert solutions using Python, R, Bash, Julia, or domain-specific software. The listing says the work can involve sensitive or graphic content.
We are seeking experienced Electrical Engineers to design, solve, and review simulation-led engineering tasks used to train and evaluate advanced AI models. The role requires strong technical fundamentals, practical simulation expertise, and the ability to produce clear, reproducible engineering solutions. We are hiring for a PAA-based project, seeking candidates with strong hands-on experience in CAD modeling and simulation tools. Main tasks listed: Develop CAD models and perform simulation/analysis for aero, mech and elec components; Validate designs using industry-standard tools (e.g., CATIA, SolidWorks, ANSYS, MATLAB); Develop realistic electrical engineering problem statements, simulation models, calculations, schematics, and supporting technical assets; Define inputs, assumptions, constraints, operating conditions, output metrics, and objective acceptance criteria.
We are seeking a highly qualified Art 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 fine arts, visual arts, architecture, design, art history, museums, and artists. Main tasks listed: Create advanced prompts covering fine arts, visual arts, architecture, design, art history, museums, and artists; 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 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 the ability to read, summarize, and break down large content into smaller logical blocks, conduct research online, validate claims made in content through online research, and work with the LLM (Large Language Models) to solve puzzles! The projects will also help you learn how to leverage AI to be a better analyst. Main tasks listed: You would spend time answering a variety of interesting analytical questions and creating scenarios that can train the LLM models to get better. Here are a couple of examples that models might get wrong, and You would articulate the correct answer & explanation to enable the models to learn:; Based on a given distribution of sales by month across locations, could you analyze which location has grown the most? (Hint: what time period should we look at? should we account for sudden variability at the beginning?); In a small town, there are four distinct neighborhoods: Oak, Pine, Maple, and Elm. A postman is assigned to deliver mail and can only deliver to two neighborhoods in one day, with certain rules (e.g. Oak is always visited before Pine). If he delivers to Oak and Elm on the first day, which neighborhoods does the carrier deliver to on the second day?; Note: No other prior specialized domain experience is needed.
Design original and challenging mathematics problems that test the reasoning limits of large language models, especially in multi-step, abstract, and proof-based settings. Solve problems independently and write detailed, logically structured solutions with clear justifications. Main tasks listed: Design original and challenging mathematics problems that test the reasoning limits of large language models, especially in multi-step, abstract, and proof-based settings; Solve problems independently and write detailed, logically structured solutions with clear justifications; Review model-generated solutions, identify mathematical errors or missing arguments, and provide precise feedback, annotations, and corrections.; Contribute to defining new evaluation benchmarks based on Mathematics curricula spanning from 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.