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About Turing: About Turing:Based in San Francisco, California, Turing is the world's leading research accelerator for frontier AI labs and a trusted partner for global enterprises deploying advanced AI systems. Turing supports customers in two ways: first, by accelerating frontier research with high-quality data, advanced training pipelines, plus top AI researchers who specialize in coding, reasoning, STEM, multilinguality, multimodality, and agents; and second, by applying that expertise to help enterprises transform AI from proof of concept into proprietary intelligence with systems that perform reliably, deliver measurable impact, and drive lasting results on the P&L.About the RoleWe 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. Tasks should test synthesis and interpretation rather than factual recall and help distinguish advanced AI reasoning capabilities.Responsibilities 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. Identify ambiguity, alternative interpretations, and insufficiently constrained questions. Ensure tasks are self-contained, reproducible where applicable, and clearly documented. Incorporate reviewer feedback while maintaining quality and throughput.Required Qualifications Ph.D., postdoctoral experience, or equivalent advanced research experience in the life sciences. Strong expertise in at least one biological discipline. Demonstrated ability to interpret scientific experiments, data, and research findings. Strong scientific writing skills and attention to precision.Relevant backgrounds include molecular biology, genetics and genomics, cell biology, biochemistry, neuroscience, microbiology, immunology, evolutionary biology, ecology, structural biology, biophysics, developmental biology, cancer biology, pharmacology, and related life sciences disciplines.Preferred Qualifications Experience designing graduate-level problems, assessments, or research exercises. Experience reviewing scientific manuscripts or research. Ability to translate complex scientific concepts into precise, self-contained questions. Experience evaluating AI-generated scientific responses or working with AI/ML evaluation datasets. Peer-reviewed publications or graduate-level teaching and mentoring experience.Important NoteThis role does not involve programming or executing code. For coding-based or workflow-oriented opportunities, please explore GeneBench Pro, Agentic Life Sciences, or Terminal Bench Science.Offer Details: Commitments Required: at least 8 hours per day and minimum 40 hours per week with 4 hours of overlap with PST.  Engagement type: Contractor assignment/freelancer (no medical/paid leave) Duration of contract: 1 monthBenefits Opportunity to work on cutting-edge AI projects. Competitive compensation. Flexible working hours and remote work environment.Evaluation Process Job interest form Assessment  Based on the answers and scores, you will be contacted to discuss the pre-onboarding requirements

About Turing: Role Overview:Turing is building one of the most rigorous STEM AI training datasets in the industry. The SciCode project involves creating high-quality scientific coding tasks that are used to train and evaluate frontier AI models. As a SciCode Trainer, you will be directly contributing to cutting-edge AI research by authoring, implementing, and reviewing complex scientific problems across core STEM disciplines.  Responsibilities: Write scientific problem specifications consisting of one main problem and a minimum of 3 sub-problems, all logically connected and progressively building toward the main problem solution Implement verified golden solutions in Python with complete unit test coverage Design discriminative test cases that clearly differentiate correct from incorrect model outputs Run QC validation checks on the Turing Central Task Platform (CTP) including Tier 1 structure checks and Tier 2 quality rubrics Iterate on tasks based on QC feedback to meet Pass@K evaluation criteria across multiple LLM judges (GPT, Gemini, Nemotron)  Maintain high output quality with a low rework rate, targeting consistent L1 approval on first submission  Participate in sync calls for reviews, feedback sessions, and project standups during overlap hoursRequired Qualifications:  Master's or PhD in  Physics. Strong Python programming skills with experience in scientific computing Ability to write rigorous, well-posed scientific problems with clear constraints and expected outputs  Attention to detail - tasks must meet strict rubrics for well-posedness, test case discriminativeness, scientific correctness, and determinism  Prior experience in AI data annotation, research, or scientific writing Familiarity with LLM evaluation frameworks or coding benchmarks Experience with libraries such as NumPy, SciPy, SymPy, or domain-specific scientific tools . Published research or academic project experience in a STEM domain Quality StandardsOffer Details: Commitments Required : Overlap of 4 hours with PST and  40 hrs/week  Engagement type : Contractor assignment/freelancer (no medical/paid leave) Duration of contract :  8 weeks  Location : Bangladesh, Brazil, Colombia, Egypt, Ghana, India, Pakistan, Indonesia, Kenya, Nigeria, Turkey, Vietnam

About Turing: Role Overview:We are staffing a frontier AI initiative that requires strong Engineering experts to develop realistic, terminal-based scientific and technical tasks used to train and evaluate AI agents.The work involves translating authentic engineering workflows into reproducible computational tasks that test modeling, simulation, optimization, data processing, debugging, and technical validation. The role spans disciplines including mechanical, electrical, chemical, aerospace, civil, materials, biomedical, robotics, and control systems engineering.What you'll do:Design realistic, multi-step terminal tasks based on real-world engineering and scientific workflows.Create engineering datasets, simulation inputs, geometry files, sensor data, design constraints, and configuration files.Develop expert solutions using Python, C/C++, Julia, MATLAB/Octave, Bash, or relevant engineering software.Build reproducible, containerized environments with appropriate engineering tools and pinned dependencies.Develop tasks involving simulation, numerical analysis, optimization, control systems, signal processing, finite-element concepts, CAD-related data, and engineering design.Create automated tests and objective grading criteria that validate engineering correctness, including units, physical constraints, tolerances, convergence, stability, boundary conditions, and numerical behavior.Debug solver, dependency, workflow, precision, and performance issues and clearly document assumptions, requirements, expected outputs, and edge cases.What we're looking for:Ph.D., postdoctoral experience, or equivalent advanced technical experience in an Engineering discipline such as mechanical, electrical, chemical, aerospace, civil, materials, biomedical, robotics, or control systems.Strong scientific programming skills in Python, C/C++, Julia, MATLAB/Octave, Bash, or another relevant language.Hands-on experience working in Linux or terminal-based environments.Experience with engineering simulation, modeling, numerical analysis, optimization, signal processing, control systems, or technical data analysis.Strong understanding of numerical methods, engineering units, physical constraints, boundary conditions, and technical validation.Ability to build, debug, and validate reproducible computational engineering workflows.Nice to have:Experience with scientific libraries such as NumPy, SciPy, pandas, matplotlib, SymPy, or PyTorch.Familiarity with engineering tools such as OpenFOAM, CalculiX, FEniCS, ROS, LTspice-compatible workflows, QEMU, or similar software.Experience with finite-element methods (FEM), computational fluid dynamics (CFD), robotics, embedded systems, control systems, or digital twins.Familiarity with Docker, Git, CI/CD, automated testing, or HPC environments.Experience developing technical benchmarks, programming tasks, simulation-based evaluations, or automated graders.Experience evaluating AI coding or terminal agents or working in research software engineering.Publications, patents, open-source contributions, or industry experience involving computational engineering.Offer Details:Commitments Required: At least 6 hours per day and minimum 40 hours per week with overlap of 4 hours with PST. Employment type  : Contractor assignment (no medical/paid leave)Duration of contract : 5 week [expected start date is next week]Payment type: Pay Per Task $300/per approved task

About Turing: About Turing:Turing is one of the world’s fastest-growing AI companies, accelerating the advancement and deployment of powerful AI systems.Turing helps customers in two ways: Working with the world’s leading AI labs to advance frontier model capabilities in thinking, reasoning, coding, agentic behavior, multimodality, multilinguality, STEM and frontier knowledge; and leveraging that work to build real-world AI systems that solve mission-critical priorities for companies.About the RoleWe 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.What You Will Produce A natural-language research question with a short, stable, objectively verifiable answer Clues, each independently checkable, spanning multiple fact types - including dates, people, places, organisations, works, events, records, and quantities - with specific constraints A validation record showing the obvious searches you ran and the results they returnedMinimum Qualifications Master's OR >3 YOE. Demonstrated open-web research ability, including locating primary records and navigating government and institutional databases, archives, registries, and PDF documents Precision with sourcing. You cite exact pages, tables, and sections—not just homepages Comfort researching unfamiliar subjects from scratch Native or near-native written English High tolerance for structured documentation. The evidence trail is the majority of the work Experience with LLM evaluation, red-teaming, or benchmark construction Experience in one or more of the following domains:   Reference librarianship, archival research, or special collections   Investigative journalism or professional fact-checking   OSINT, due diligence, KYC, or investigative research   Patent, prior-art, or legal-discovery search   Genealogy and records research   Competitive quizzing or puzzle-hunt constructionNice to Have Familiarity with JSON and structured data delivery formatsProject Snapshot Current task mix provides estimated earning potential of approximately $30 per approved task. 8 week project Ideally upto 40 hours/week  Fully remote | Paid in USD Start as soon as you successfully pass the assessmentWhat’s Next? Submit your application and complete the required assessment. Candidates who successfully pass the assessment will be eligible to proceed with onboarding.

About Micro1: Job DescriptionSTEM ExpertPay: $60–$120/hourLocation: Global, fully remoteJob Type: Contractor (~15 hours per week)Schedule: Flexible—you choose the hours and days you work, including weekends if desiredWe are looking for highly skilled STEM and scientific-domain experts to contribute to an AI training project involving technical problem-solving, scientific reasoning, data analysis, and Python.The work involves solving, reviewing, and validating challenging problems within your area of expertise. A representative task may require analyzing a scientific or mathematical problem, developing a rigorous solution, using Python to perform calculations or simulations, and evaluating whether an AI-generated answer is technically correct.This is a very coding heavy role. Candidates but have experience using coding agents in their workflow.Candidates are not expected to be experts across every STEM discipline. We are looking for deep expertise in at least one of the following areas:ChemistryBiologyMathematicsPhysicsEarth Science / Geoscience / Environmental ScienceRoboticsAdjacent scientific or quantitative disciplines may also be considered where the candidate's expertise is directly relevant.What You’ll Work OnSolve complex scientific, mathematical, or technical problems within your domain of expertise.Review AI-generated solutions for factual, mathematical, and scientific correctness.Identify incorrect assumptions, reasoning errors, missing constraints, and subtle technical mistakes.Required QualificationsStrong expertise in at least one relevant STEM or scientific domain.Practical proficiency with Python or other coding languages.Strong scientific, mathematical, or quantitative problem-solving ability.Relevant Python tools may include NumPy, SciPy, pandas, SymPy, Jupyter, or domain-specific scientific libraries. No particular Python library is mandatory.Expertise may come from academic research, industry, teaching, engineering, independent technical work, or other demonstrated experience. A specific degree or number of years of experience is not strictly required if you can demonstrate strong expertise in your field.ProcessApply to the role and complete the screening questions.Complete an AI interview of approximately 30 minutes.Complete the hiring manager review.Compensation StructureCompensation is output-based. Experts are paid per task that meets the project specifications. The time required to complete each task may vary depending on the expert’s experience and workflow.Minimum submission requirements apply.Start Timeline & AvailabilityWe typically fill roles within 48 hours and are looking for experts who are ready to begin immediately. If selected, you will be expected to start your first task within 24–48 hours of completing onboarding.