Science & Maths AI Training Jobs - Compare Pay

micro1

Simulation Expert — Real-to-Sim 3D Environments & Assets

micro1STEM

$60-$150/hr

Worldwide

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Mercor

Physics Expert

MercorSTEM

$70-$100/hr

Worldwide

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About this role

The research teams behind the best-known AI models come to Mercor for physics judgment they can't generate on their own. Your standards and expertise become the rubric that frontier AI is graded against. This is a standing listing for physicists who want AI training and evaluation work, not a specific job opening. It stays open whether or not a matching project is running this week. New projects start on short notice, so instead of posting and pulling a listing each time, we keep one open per field and draw from it when one begins. What the work looks like We match physicists in the network to projects like: - Writing a problem from your own area, with the system, the assumptions, and the regime, plus the reference answer a model's attempt gets graded against - Grading model-written derivations and numerical work: whether the approximation is valid in the regime claimed, whether the limiting cases check out, whether the units and scaling survive scrutiny - Flagging where a model reaches the right final expression through reasoning that would not hold if the assumptions changed Past rates have run $70 to $100 an hour, set per project by scope and depth. What we're looking for - A graduate degree in physics or a related field, with research experience - Clear written communication, since most of the work involves explaining your reasoning - Comfort with ambiguous tasks, and willingness to flag unclear instructions Where you trained matters less than whether you did the derivations and the analysis yourself, recently enough to explain the calls you made. How it works Apply here with your resume and confirm where you work from, then take a short AI interview, about 20 minutes total. We verify your background and add you to the physics pool. When a project needs physics expertise, we invite matching people from the pool to that specific listing. It names the rate, the hours, and the client, and any hiring decision is made there. What to expect This listing does not accept or reject candidates, so you will not get a decision on it. We reach out when a project matches your background. Depending on what our clients are running, that can be within a week or several months. Finishing the domain expert interview and joining every network you qualify for both improve your odds of being matched. You only need to sign up once to be eligible across all of them.

SME

Chemist

SMESTEM / Education and Humanities / Software, AI and Data

Up to $40/hr

Brazil

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About this role

We’re seeking chemists to join our AI training project. In this role, you will review AI-generated responses, evaluate the reasoning quality and step-by-step problem-solving, and provide expert feedback that helps models produce answers that are accurate, logical, and clearly explained. You will assess solutions for accuracy, clarity, and adherence to the prompt; identify errors in calculations, methodology, or conceptual understanding; fact-check chemical information across; write high-quality chemical explanations and model solutions that demonstrate correct methods; and rate and compare multiple AI responses based on chemical correctness and reasoning quality. Requirements - You hold an MS or PhD in Chemistry or a related field in a high ranking university. - Confident in chemical reactions and stoichiometry, organic mechanisms, analytical methods, and physical/quantum chemistry. - Expert at fact-checking information using public sources across multiple domains. You communicate chemistry clearly and turn advanced theory into real-world solutions. - English proficiency C1 and above with excellent writing skills. - Background in research, analytical writing, debate, programming, or chemistry is preferred. - Bonus: Data labeling, RLHF, or AI model evaluation experience. - You enjoy applying chemical reasoning to evaluate and improve AI capabilities. - For PHD holders: you have developed or critically reviewed complex chemistry content (e.g., problem banks, proofs, textbook sections, research notes). Responsibilities - Develop AI Training Content: Create detailed prompts in various topics and responses to guide AI learning, ensuring the models reflect a comprehensive understanding of diverse subjects. - Optimize AI Performance: Evaluate and rank AI responses to enhance the model's accuracy, fluency, and contextual relevance. - Ensure Model Integrity: Test AI models for potential inaccuracies or biases, validating their reliability across use cases. Skills - Research - Teaching - Analytical Chemistry - Organic Chemistry - Medicinal Chemistry - Reasoning Skills - Chemistry - English - Inorganic Chemistry - Writing Additional skills - Organic Chemistry - Physical Chemistry - Analytical Chemistry - Quantum Chemistry - Chemical Kinetics - Thermodynamics - Reaction Mechanisms - Stoichiometry - Spectroscopy - NMR Spectroscopy - Mass Spectrometry (MS) - Infrared Spectroscopy (IR) - UV-Vis Spectroscopy - Chromatography (HPLC/GC) - Titration - Method Development - Data Analysis - Scientific Writing - Technical Editing - Literature Review - Fact-Checking - Peer Review - Research Design - Experimental Design - Statistical Analysis - Python - Jupyter Notebook - Machine Learning - AI Model Evaluation - Data Labeling

DataAnnotation

Experimental Biologist

DataAnnotationLife Sciences

Up to $40–$125+ / hour

Worldwide

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About this role

Overview We’re looking for experienced bench scientists to help train AI models. You’ll design realistic experimental-biology tasks from your own practice, run them through frontier AI systems, and grade what comes back against the standard you’d hold a colleague to. The models can already talk fluently about biology. What they can’t yet do reliably is the real work: call a plate QC go/no-go, gate a flow panel, check a cloning strategy, or read a Western blot from the raw images. Your bench judgment becomes the benchmark those models are measured against. What you’ll actually do - Design realistic experimental tasks from your own workflows: the scenario, the prompt, and the supporting files an agent would need (plate maps and reader output, gel and blot images, flow summaries, protocols, QC records). - Run tasks through frontier AI agents and grade the deliverable they produce (a QC summary, experimental plan, or troubleshooting memo) against a professional standard. - Write clear grading rubrics that specify what a correct deliverable must contain (the right controls, the right wells excluded, the right diagnosis of a failed run), and explain why a response passes or fails. - Flag concrete failures with evidence: controls ignored, edge effects and outliers missed, wrong normalization, plausible-sounding troubleshooting the raw data contradict, or fabricated values. Problems draw on whatever you know best: - Molecular and cell biology: cloning, cell culture, and cell-based assays. - Protein biochemistry: purification, characterization, and assay development. - Immunology and flow cytometry: panel design and gating. - Imaging and histology, microbiology and virology. - In vivo and preclinical work, plus lab operations and analytical sciences. Roles this fits Common backgrounds: Research Scientist, Molecular / Cell Biologist, Postdoctoral Fellow. What we look for - 3+ years designing and running experiments in a pharma, biotech, or academic research lab (counted after undergraduate study). - Depth in at least one of: molecular biology and cloning; cell culture and cell-based assays; protein biochemistry; immunology and flow cytometry; imaging and histology; microbiology or virology; in vivo and preclinical work. - A hands-on practitioner who designs and troubleshoots experiments, not only executes SOPs: you can explain what each control is for and diagnose why a run failed. - You generate and QC primary data, and can judge from raw instrument output whether a run is usable. - Master’s or PhD (or current PhD candidate) in biology or a related field, completed in the U.S., Canada, Europe, or the UK. - Clear written English, comfort with ambiguity, and familiarity with AI/LLM tools like Claude or ChatGPT. - Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). How it works Apply Qualify Work & get paid Compensation Up to $125+/hr depending on task difficulty and specialization. Many contributors add $100k–$200k a year; some make it their full-time income. About DataAnnotation DataAnnotation is where 1M+ experts train the world’s leading AI models. Hundreds of millions paid to contributors to date, and the average contributor stays 5+ years. Flexible, remote, and always project-available.

micro1

Machine Learning Researcher (MSc. or PhD)

micro1Software, AI and Data / STEM

$60-$120/hr

Worldwide

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About this role

Machine Learning Researcher (MSc. or PhD) Pay: $60–$120/hour Location: Global, fully remote Job Type: Contractor (~15 hours per week) Schedule: Flexible—you choose the hours and days you work, including weekends if desired We are looking for highly skilled Machine Learning Experts to contribute to an AI training project involving model development, training and inference systems, numerical computing, performance optimization, and Python. The work involves creating, solving, reviewing, and validating challenging machine-learning engineering tasks. A representative task may require implementing or modifying a model, constructing a reproducible training or inference workflow, optimizing memory or throughput, debugging numerical or system-level failures, and verifying that the resulting implementation satisfies objective correctness and performance requirements. This is a very coding heavy role. Candidates but have experience using coding agents with python in their workflow. What You’ll Work On 1. Develop and validate machine-learning models, training pipelines, inference systems, and supporting infrastructure. 2. Implement model components, data pipelines, evaluation systems, and numerical methods. 3. Build reproducible programmatic workflows using Python and command-line tools. Required Qualifications 1. A master’s degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, or a closely related quantitative discipline. 2. Strong professional or research experience in machine learning. 3. Practical proficiency with Python and coding agents. Equivalent tools may also be considered when the candidate demonstrates directly relevant depth. Experience at a well-established technology company, AI laboratory, research organization, or other recognized engineering environment is strongly preferred. Exceptional open-source or academic experience may also qualify. Process 1. Apply to the role and complete the screening questions. 2. Complete an AI interview of approximately 30 minutes. 3. Complete the hiring manager review. Compensation Structure Compensation 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 & Availability 1. We 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.

Mercor

Analytical Chemist

MercorSTEM

$65-$75/hr

Worldwide

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About this role

About the work Mercor is assembling a panel of chemistry and chemical safety experts to red-team frontier AI models. The goal is to test whether a model can correctly judge the misuse potential of a technical request — answering legitimate questions fully while refusing genuinely dangerous ones. You will: - Write challenging single-turn prompts in your domain, labelled across three levels: benign, dual-use, and adversarial. - Evaluate the responses that come back against a defined policy standard, and judge whether each was handled correctly. - Write the reference answer — what a correct response looks like, and the technical reasoning for why. This domain lives on the dual-use line more than most. The same method that detects a hazardous compound at trace level defines the threshold someone would try to stay beneath; the same library entry that identifies an unknown tells you what a screen will and won't see. A model that refuses a routine method-validation question is failing as badly as one that answers a question it shouldn't. Drawing that line reliably is why we need practitioners rather than generalists. Background we're looking for We're looking for analysts who have built and validated methods for hazardous compounds — people who know what a detection limit really means and what it implies. What matters most is being able to tell a routine professional question from one that's fishing for something else: the same detection method that finds a compound describes how to evade the screen. You will be writing prompts that sit exactly on that line, then judging whether the model held it. Examples of relevant backgrounds (ideally with red-teaming experience): - Detection and identification of hazardous compounds — GC-MS, LC-MS/MS, IR, NMR - Method development and validation for trace-level analysis - Environmental, occupational or emergency-response sampling and analysis - Reference standards, spectral libraries and unknown identification - Laboratory accreditation, QA/QC and proficiency testing What this role demands beyond technical depth This is writing-intensive work. Every judgment you make needs a written rationale that a non-specialist can follow. Prior technical writing, published research, or expert witness experience is a strong signal; please include a sample or link. You will also be reading and writing about misuse scenarios in your field for sustained periods. We brief experts on this in advance, and you can pause or step away at any point without penalty. Before you apply Your work here will not involve, and must not draw on, classified or export-controlled information, or anything covered by an NDA or prepublication review obligation. If you hold such obligations you may still be a good fit; tell us in your application and we will scope the work accordingly.

micro1

Senior Metallurgist / Welding Inspector

micro1STEM

$80-$130/hr

Worldwide

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About this role

Role Title: Senior Metallurgist / Welding Inspector Pay: $40–$60/hour Location: Global, fully remote Job Type: Contractor (~10 hours per week) Schedule: Flexible—you can choose the hours and days you work, including weekends if desired as long as you're active micro1 is engaging expert Senior Metallurgists and Welding Inspectors to participate in a high-impact project for a customer in advanced manufacturing and materials engineering. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters. Scope of Work 1. Evaluate and interpret metallographic microstructures to determine material acceptability and conformance to standards. 2. Conduct heat treatment process analysis and acceptance reviews in accordance with AMS 2750, ASTM E112, and related US specifications. 3. Perform coating inspection tasks aligned with NACE/AMPP and SSPC protocols, ensuring surface integrity and protection requirements are met. 4. Review and disposition additive manufacturing (LPBF) defects based on detailed build and cross-sectional data. 5. Reference and apply NADCAP, AWS, ASTM, and AMS standards to a variety of metal samples, weldments, coatings, and AM builds. Preferred Qualifications 1. At least 8 years of hands-on experience making high-stakes accept/reject calls on micrographs, weld macros, fracture surfaces, coatings, or LPBF additive builds. 2. Advanced knowledge of US and international standards including ASTM, AMS, AWS, NADCAP, NACE/AMPP, and SSPC. 3. Current or prior AWS Certified Welding Inspector (CWI) credentials, or equivalent metallurgical certification. 4. Familiarity with additive manufacturing processes, especially laser powder bed fusion (LPBF) and associated defect mechanisms. Process 1. Apply to the role and complete the screening questions. 2. Complete an AI interview of approximately 20-30 minutes. 3. Complete the hiring manager review. Start Timeline & Availability We 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.

SME

Physicist

SMESTEM / Software, AI and Data

Up to $30/hr

India

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About this role

We’re seeking physicists to join our AI training project. In this role, you will review AI-generated responses, evaluate the reasoning quality and step-by-step problem-solving, and provide expert feedback that helps models produce answers that are accurate, logical, and clearly explained. You will assess solutions for accuracy, clarity, and adherence to the prompt; identify errors in calculations, methodology, or conceptual understanding; fact-check physics information; write high-quality physics explanations and model solutions that demonstrate correct methods; and rate and compare multiple AI responses based on physics correctness and reasoning quality. Requirements - You hold an MS or PhD in Physics or a related field in one of the top 100 universities. - Confident in physical reasoning, mechanics, electromagnetism, optics, thermodynamics/statistical mechanics, and modeling/systems thinking - You have developed or critically reviewed complex physics content (e.g., problem banks, proofs, textbook sections, research notes). - Expert at fact-checking information using public sources across multiple domains. - You communicate physics clearly and turn advanced theory into real-world solutions. - Background in research, analytical writing, debate, programming, or physics is preferred. - Published in a peer-reviewed journal. - English proficiency C1 and above with excellent writing skills. - Bonus: Data labeling, RLHF, or AI model evaluation experience. Responsibilities - Develop AI Training Content: Create detailed prompts in various topics and responses to guide AI learning, ensuring the models reflect a comprehensive understanding of diverse subjects. - Optimize AI Performance: Evaluate and rank AI responses to enhance the model's accuracy, fluency, and contextual relevance. - Ensure Model Integrity: Test AI models for potential inaccuracies or biases, validating their reliability across use cases. Skills - Nuclear Physics - Research - Computational Physics - Reasoning - Physics - Laser Physics - Experimental Physics - Medical Physics - English - Theoretical Physics Additional skills - Classical Mechanics - Electromagnetism - Optics - Thermodynamics - Statistical Mechanics - Quantum Mechanics - Mathematical Physics - Computational Physics - Physical Modeling - Systems Thinking - Numerical Methods - Scientific Computing - Data Analysis - Python - MATLAB - C++ - Scientific Writing - Analytical Writing - Peer-Reviewed Publishing - Literature Review - Fact-Checking - Research Methods - Problem Solving - Critical Thinking - Technical Communication - Content Development - Proofreading & Editing - Peer Review - AI Model Evaluation - Data Labeling

Mercor

Physicist Talent Network

MercorSTEM

$60-$80/hr

Worldwide

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micro1

CAD Design Expert (Instructor)

micro1STEM / Education and Humanities

$40-$90/hr

Worldwide

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About this role

micro1 is looking for experienced Mechanical Design Engineers and CAD Experts to contribute to an AI training project focused on complex mechanical assemblies. You'll use your practical CAD and mechanical design expertise to create challenging technical questions, analyze how mechanical components fit and move together, and help AI models develop a better understanding of real-world engineering. What You'll Do 1. Analyze complex 3D CAD assemblies, including how components fit together, move, and interact. 2. Create challenging, technically accurate questions about mechanical assemblies, with clear and verifiable answers. 3. Apply your understanding of joints, mates, constraints, degrees of freedom, clearances, and tolerances to solve assembly-related problems. 4. Explain the engineering reasoning behind your answers in a clear, structured way. 5. Review and validate technical questions and solutions to ensure accuracy and consistency. 6. Collaborate remotely with the project team and incorporate feedback to improve the quality of AI training materials. What We're Looking For 1. Ideally 5+ years of hands-on mechanical design experience, particularly with complex, multi-part CAD assemblies. 2. Strong proficiency in at least one parametric CAD tool, such as SolidWorks, Creo, CATIA, Siemens NX, Inventor, Fusion 360, Onshape, or Solid Edge. 3. Practical experience designing mechanical systems with moving components, such as machinery, robotics, automation equipment, automotive mechanisms, or electromechanical products. 4. Good understanding of assembly constraints, mechanical motion, component interactions, tolerances, and design intent. 5. Ability to explain complex mechanical concepts clearly in written and spoken English. 6. Strong attention to detail and the ability to develop precise, technically correct solutions. Nice to Have 1. Experience with FreeCAD. 2. Experience teaching, mentoring, or reviewing mechanical design work. 3. Professional CAD certifications, such as CSWP or CSWE. 4. Experience developing technical exercises, engineering assessments, or educational materials.

SME

Biochemist

SMELife Sciences

Up to $30/hr

6 countries

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About this role

In this hourly, remote contractor role, you will work as a Biochemistry Expert (SME) to review AI-generated biochemistry content and/or create high-quality biochemistry reference content, evaluating scientific accuracy, reasoning quality, terminology, and step-by-step explanations while providing precise written feedback. You will assess outputs for correctness, clarity, biochemical reasoning, calculation accuracy where relevant, experimental context, safety awareness, and adherence to the prompt; identify errors in molecular mechanisms, pathways, enzyme kinetics, protein structure, metabolism, genetics, lab methods, and scientific interpretation; fact-check claims when needed; write high-quality rewrites and model answers that demonstrate best practices in biochemistry explanation; and rate and compare multiple AI responses based on correctness and overall quality. 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 biochemistry expertise directly helps improve the world’s premier AI models by making their biochemical, molecular biology, and life-science outputs more accurate, reliable, and clearly explained. Requirements - Bachelor’s degree or higher in Biochemistry, Molecular Biology, Biology, Chemistry, Biotechnology, Biomedical Sciences, Pharmacology, or a related field, or equivalent professional experience. - Strong professional proficiency in English, minimum C1, to follow guidelines and provide detailed feedback in English. - 3+ years of experience in biochemistry research, laboratory work, teaching, tutoring, scientific writing, academic review, life-science QA, or related scientific work. - Deep understanding of proteins, enzymes, metabolism, nucleic acids, carbohydrates, lipids, cell signaling, molecular biology, and biochemical techniques. - Ability to evaluate biochemical explanations, mechanisms, pathways, figures, calculations, and experimental interpretations for scientific accuracy and clarity. - Comfortable identifying hallucinated claims, incorrect mechanisms, unsafe lab advice, flawed reasoning, misleading simplifications, or unsupported biological/chemical statements. - High attention to detail when reviewing terminology, reaction logic, pathway directionality, units, concentrations, lab protocols, and data interpretation. - Reliable, self-directed, and able to deliver consistent quality in an hourly, remote contractor workflow across time zones. - Prior experience with AI data training/annotation, scientific QA, academic QA, content review, or rubric-based evaluation is strongly preferred. Responsibilities - Develop AI Training Content: Create prompts and gold-standard biochemistry answers across topics such as metabolism, enzyme kinetics, protein structure, molecular biology, laboratory methods, genetics, and biochemical regulation. - Optimize AI Performance: Evaluate and rank AI outputs to improve scientific accuracy, reasoning quality, terminology, structure, clarity, and relevance to the user’s prompt. - Ensure Model Integrity: Detect unsafe or misleading scientific content, hallucinated mechanisms, incorrect pathways, flawed calculations, unsupported claims, and overconfident interpretations; validate reliability across life-science use cases. Skills - Enzyme Kinetics - Lab Methods - Metabolism - Molecular Biology - Biochemistry - Protein Structure - Fact-Checking - AI Training - English - Scientific Review Additional skills - Biochemistry - Molecular Biology - Protein Biochemistry - Enzymology - Metabolic Pathways - Cell Signaling - Nucleic Acid Biochemistry - Carbohydrate Biochemistry - Lipid Biochemistry - Recombinant Protein Expression - Protein Purification - SDS-PAGE - Western Blotting - ELISA - PCR - qPCR - DNA/RNA Extraction - Molecular Cloning - Gel Electrophoresis - Chromatography (HPLC/FPLC) - Mass Spectrometry - Spectrophotometry - Enzyme Kinetics - Cell Culture - Microscopy - Experimental Design - Data Analysis - Statistical Analysis - Scientific Writing - Peer Review - Quality Assurance (QA)

Mercor

Chemist Talent Network

MercorSTEM

$60-$80/hr

Worldwide

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micro1

Principal Engineer / Professor — Technical Expert

micro1STEM7 days ago

$85-$185/hr

Worldwide

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About this role

Opportunity Snapshot Pay: $80- $185/hour Commitment: Flexible — fits alongside your current role Location: Fully remote You’ll need: Master’s or PhD in an engineering or physical science field, plus principal-level industry experience or a faculty appointment Your tasks: Build challenging technical problems from your own domain and define what makes an answer correct micro1 is engaging principal-level engineers, scientists, and faculty to design the technical problems that measure what AI systems can actually do. Your domain expertise is what matters here — no prior AI experience required. We’re building this across four areas: aerospace, semiconductors, manufacturing and industrial engineering, and materials science. If your work sits in one of those — or across two of them — this is for you. The work draws on what you already know: the problems where the textbook answer isn’t enough, where the right approach depends on reading the constraints correctly, where a plausible-looking solution is quietly wrong. What you’ll do 1. Design difficult technical problems from your own area, with realistic constraints and supporting data. 2. Provide the correct solution and walk through the reasoning that gets there. 3. Define objective criteria for correctness, so another expert reviewing the work would reach the same verdict. 4. Review problems built by other contributors for technical accuracy and soundness of method. 5. Flag problems that turn out to be ambiguous or ungradable rather than submitting them. What we’re looking for 1. Master’s or PhD in engineering, physics, materials, or a closely related field. 2. Principal, staff, or fellow-level experience at a major engineering organization, a national lab, or a faculty appointment at a leading university. 3. Deep technical depth in at least one area, with hands-on ownership of the work. 4. Able to explain why an answer is correct clearly enough that another expert could verify it. 5. Publications, patents, or a record of technical leadership is a strong plus. How the selection process works 1. Apply and complete the screening questions 2. Complete the AI interview for this role 3. Profile review 4. Onboarding — our Talent Ops team will get you set up

SME

Chemist

SMESTEM / Education and Humanities / Software, AI and Data

Up to $30/hr

India

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About this role

We’re seeking chemists to join our AI training project. In this role, you will review AI-generated responses, evaluate the reasoning quality and step-by-step problem-solving, and provide expert feedback that helps models produce answers that are accurate, logical, and clearly explained. You will assess solutions for accuracy, clarity, and adherence to the prompt; identify errors in calculations, methodology, or conceptual understanding; fact-check chemical information across; write high-quality chemical explanations and model solutions that demonstrate correct methods; and rate and compare multiple AI responses based on chemical correctness and reasoning quality. Requirements - You hold an MS or PhD in Chemistry or a related field in a high ranking university. - Confident in chemical reactions and stoichiometry, organic mechanisms, analytical methods, and physical/quantum chemistry. - Expert at fact-checking information using public sources across multiple domains. You communicate chemistry clearly and turn advanced theory into real-world solutions. - English proficiency C1 and above with excellent writing skills. - Background in research, analytical writing, debate, programming, or chemistry is preferred. - Bonus: Data labeling, RLHF, or AI model evaluation experience. - You enjoy applying chemical reasoning to evaluate and improve AI capabilities. - For PHD holders: you have developed or critically reviewed complex chemistry content (e.g., problem banks, proofs, textbook sections, research notes). Responsibilities - Develop AI Training Content: Create detailed prompts in various topics and responses to guide AI learning, ensuring the models reflect a comprehensive understanding of diverse subjects. - Optimize AI Performance: Evaluate and rank AI responses to enhance the model's accuracy, fluency, and contextual relevance. - Ensure Model Integrity: Test AI models for potential inaccuracies or biases, validating their reliability across use cases. Skills - Research - Teaching - Analytical Chemistry - Organic Chemistry - Medicinal Chemistry - Reasoning Skills - Chemistry - English - Inorganic Chemistry - Writing Additional skills - Organic Chemistry - Physical Chemistry - Analytical Chemistry - Quantum Chemistry - Chemical Kinetics - Thermodynamics - Reaction Mechanisms - Stoichiometry - Spectroscopy - NMR Spectroscopy - Mass Spectrometry (MS) - Infrared Spectroscopy (IR) - UV-Vis Spectroscopy - Chromatography (HPLC/GC) - Titration - Method Development - Data Analysis - Scientific Writing - Technical Editing - Literature Review - Fact-Checking - Peer Review - Research Design - Experimental Design - Statistical Analysis - Python - Jupyter Notebook - Machine Learning - AI Model Evaluation - Data Labeling

Mercor

Physics Domain Expert

MercorSTEM8 days ago

$90-$140/hr

United States

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About this role

Help define how frontier AI models reason about physics. 1. Overview We are looking for senior physicists to improve how frontier AI models reason about real physics research. Your scientific judgment is the substance of this role. You will review the quality of physics reasoning tasks and model outputs, write the problem specifications and reference solutions that define what "correct" looks like, and design benchmarks that show whether a model is genuinely improving. We are looking for a practicing specialist rather than a generalist. This role is fully remote. 2. Key Responsibilities - Expert review: Assess physics tasks and model outputs, catching flawed derivations, unjustified approximations, dimensional and limit errors, and answers that read well but would not survive peer review. - Problem specs and reference solutions: Write precise problem statements and rigorous reference solutions that reflect how research physics is actually done. - Benchmarks: Design challenging, research-level problems and evaluation sets in your area of specialization. - Calibration: Work with other researchers and experts in adjacent fields to turn tacit expert judgment into explicit, teachable criteria. 3. Core Qualifications - Education: PhD in physics, astrophysics, applied physics, or a closely related field. - Seniority: A faculty appointment, principal investigator, staff or senior scientist, or an equivalent senior research role at a university, national laboratory, or research organization. - Domain depth: Genuine specialization in at least one area, for example condensed matter, high-energy or particle physics, astrophysics and cosmology, atomic, molecular, optical and quantum physics, or statistical and computational physics. - Research record: A strong peer-reviewed publication record. Recognition such as competitive fellowships or awards is a plus. - AI fluency: Hands-on use of large language models in your work, and the judgment to tell a sound argument from a plausible-sounding wrong one. - Communication: Excellent written communication and the ability to give precise, well-structured feedback. - Location: Based in the United States.

micro1

Semiconductor Engineering Expert

micro1STEM7 days ago

$85-$185/hr

Worldwide

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Opportunity Snapshot Pay: $80- $185/hour Commitment: Flexible — fits alongside your current role Location: Fully remote You’ll need: Master’s or PhD in electrical engineering, materials, physics, or a closely related field, plus principal-level industry experience or a faculty appointment Your tasks: Build challenging technical problems from your own domain and define what makes an answer correct You'll be working on The problems where the textbook answer isn’t enough, where the right approach depends on reading the constraints correctly, where a plausible-looking solution is quietly wrong. You’ll build those problems, provide the correct analysis, and define what separates a right answer from one that only looks righ What you’ll do 1. Design difficult technical problems from your own area — device physics, process integration, circuit design, lithography, packaging, yield and reliability — with realistic constraints and supporting data. 2. Provide the correct solution and walk through the reasoning that gets there. 3. Define objective criteria for correctness, so another expert reviewing the work would reach the same verdict. 4. Review problems built by other contributors for technical accuracy and soundness of method. 5. Flag problems that turn out to be ambiguous or ungradable rather than submitting them. What we’re looking for 1. Master’s or PhD in electrical engineering, materials science, applied physics, or a closely related field. 2. Principal, staff, or fellow-level experience at a major semiconductor company, or a faculty appointment at a leading university. 3. Deep technical depth in at least one semiconductor subdomain, with hands-on ownership of the work. 4. Able to explain why an answer is correct clearly enough that another engineer could verify it. 5. Publications, patents, or a record of technical leadership is a strong plus. 6. AI or LLM evaluation experience is a plus, but not required. How the selection process works 1. Apply and complete the screening questions 2. Complete the AI interview for this role — it’s how your expertise gets in front of the client 3. Profile review 4. Onboarding — our Talent Ops team will get you set up

DataAnnotation

Computational Biologist

DataAnnotationLife Sciences / Software, AI and Data

Up to $40–$125+ / hour

Worldwide

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About this role

Overview We’re looking for experienced computational biologists and bioinformaticians to help train AI models. You’ll design realistic analysis tasks from your own practice, run them through frontier AI systems, and grade what comes back against a professional standard. The models can talk fluently about biological data analysis. What they can’t yet do reliably is the real work: QC an RNA-seq count matrix, prioritize variants, diagnose a batch effect, or audit a pipeline. Your judgment of when a result is real becomes the benchmark those models are measured against. What you’ll actually do - Design realistic analysis tasks from your own workflows: the scenario, the prompt, and the files an agent would need (count matrices, sample sheets and metadata, VCFs, pipeline logs and QC output, analysis notebooks). - Run tasks through frontier AI agents and grade the deliverable (an analysis report, annotated table, figure set, or notebook) against the standard you’d hold a colleague to. - Write clear grading rubrics (the right normalization, the right multiple-testing correction, the right biological reading) and explain why a response passes or fails. - Flag concrete failures with evidence: batch effects and confounders ignored, wrong statistical test or uncorrected p-values, misused reference version, silently dropped samples, or code that doesn’t do what the narrative claims. Problems draw on whatever you know best: - Genomics and variant interpretation. - Bulk and single-cell transcriptomics. - Proteomics, metabolomics, and multiomics integration. - Population and statistical genetics. - Machine learning applied to biology. Roles this fits Common backgrounds: Bioinformatics Scientist, Genomics Data Scientist, Computational Biologist. What we look for - 3+ years hands-on analyzing real biological data in industry, an academic lab, or a research institute (counted after undergraduate study). - You write, debug, and can explain your own analysis code in Python and/or R (at least one required, both preferred). - Depth in at least one of: genomics and variant interpretation; bulk or single-cell transcriptomics; proteomics / metabolomics / multiomics; population and statistical genetics; ML applied to biology; clinical genomics; metagenomics or phylogenetics. - You’ve owned a multistep analysis end to end, from raw or messy data to final interpretation, and can judge whether a result is real (normalization, batch effects, multiple-testing correction, biological interpretation). - Master’s or PhD (or current PhD candidate) in biology, bioinformatics, biostatistics, computer science, or a related field, completed in the U.S., Canada, Europe, or the UK. - Clear written English, comfort with ambiguity, and familiarity with AI/LLM tools like Claude or ChatGPT. - Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). How it works Apply Qualify Work & get paid Compensation Up to $125+/hr depending on task difficulty and specialization. Many contributors add $100k–$200k a year; some make it their full-time income. About DataAnnotation DataAnnotation is where 1M+ experts train the world’s leading AI models. Hundreds of millions paid to contributors to date, and the average contributor stays 5+ years. Flexible, remote, and always project-available.

SME

Astronomer Quality Assurance Lead (QAL)

SMESTEM / Business and Consulting / Software, AI and Data

Up to $110/hr

United States

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In this hourly, remote contractor role, you will work as an Astronomy / Astrophysics Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across astronomy and astrophysics AI training projects. You will review AI-generated astronomy/astrophysics 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, physical reasoning, mathematical correctness, terminology quality, unit handling, observational context, 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 astronomy/astrophysics expertise, strong English communication skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote expert 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 astronomy/astrophysics quality leadership will directly help improve the world’s premier AI models by ensuring that astronomy and astrophysics training data is accurate, physically sound, clearly explained, well-documented, and aligned with client expectations. Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter. Important: There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network. Requirements - Bachelor’s, Master’s, or PhD degree in Astronomy, Astrophysics, Physics, Space Science, Planetary Science, Cosmology, or a closely related field. - Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear written feedback. - 3+ years of experience in astronomy/astrophysics research, teaching, science communication, academic review, data analysis, observatory work, or related scientific workflows. - Strong understanding of celestial mechanics, stellar evolution, galaxies, cosmology, electromagnetic radiation, observational methods, spectroscopy, planetary systems, black holes, and scientific uncertainty. - Ability to evaluate astronomy/astrophysics content against detailed rubrics and identify issues such as incorrect physical assumptions, wrong units, flawed calculations, hallucinated facts, misleading explanations, or oversimplified conclusions. - Familiarity with tools or methods such as Python, astronomical datasets, telescope/observatory data, spectroscopy, photometry, simulations, LaTeX, Jupyter notebooks, or scientific visualization is preferred. - Experience leading or supporting remote teams of researchers, educators, reviewers, annotators, science writers, 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, calibration tasks, and documentation. - Experience with AI training, data annotation, LLM evaluation, scientific QA, academic review, or rubric-based review is a strong plus. Responsibilities - Quality monitoring: Spot-check astronomy/astrophysics items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues. - Scientific review: Evaluate AI-generated astronomy/astrophysics explanations, calculations, diagrams, observational interpretations, comparisons, and step-by-step reasoning for accuracy and clarity. - Trainer and QA communication: Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and astronomy/astrophysics-specific review standards. - Question handling: Respond to trainer/QA questions clearly and promptly, especially around physical assumptions, units, astronomical terminology, observational methods, formulas, 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 astronomy/astrophysics 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 astronomy/astrophysics-specific review requirements. - Quality alignment: Ensure all trainers and QAs apply astronomy/astrophysics review guidelines consistently and understand updates as projects evolve. - Risk review: Flag misleading, overconfident, physically impossible, numerically incorrect, or poorly sourced astronomy/astrophysics claims. - Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for astronomy/astrophysics AI training projects. Skills - Astrophysics - Scientific Reasoning - LLM Evaluation - Stellar Evolution - Observational Astronomy - Astronomy - Physics Review - Cosmology - AI Training - Trainer Feedback Additional skills - Astronomy - Astrophysics - Cosmology - Celestial Mechanics - Stellar Evolution - Galaxy Formation - Black Holes - Planetary Science - Electromagnetic Radiation - Observational Astronomy - Spectroscopy - Photometry - Telescope Data Analysis - Astronomical Data Analysis - Scientific Uncertainty Analysis - Python - Jupyter Notebook - NumPy - Pandas - Matplotlib - LaTeX - Scientific Visualization - Simulations - Peer Review - Rubric-Based Evaluation - Quality Assurance (QA) - Scientific Writing - Data Annotation - LLM Evaluation - Remote Team Leadership

Mercor

Mechanical & CAD Engineers (Must own MacBook)

MercorSTEM

$50-$60/hr

Worldwide

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micro1

Materials Science Expert

micro1STEM7 days ago

$85-$185/hr

Worldwide

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About this role

Opportunity Snapshot Pay: $80- $185/hour Commitment: Flexible — fits alongside your current role Location: Fully remote You’ll need: Master’s or PhD in materials science or a closely related field, plus principal-level industry experience or a faculty appointment Your tasks: Build challenging technical problems from your own domain and define what makes an answer correct You'll be working on The problems where the textbook answer isn’t enough, where the right approach depends on reading the constraints correctly, where a plausible-looking solution is quietly wrong. You’ll build those problems, provide the correct analysis, and define what separates a right answer from one that only looks right. What you’ll do 1. Design difficult technical problems from your own area — structure-property relationships, processing, characterization, failure analysis, computational materials — with realistic constraints and supporting data. 2. Provide the correct solution and walk through the reasoning that gets there. 3. Define objective criteria for correctness, so another expert reviewing the work would reach the same verdict. 4. Review problems built by other contributors for technical accuracy and soundness of method. 5. Flag problems that turn out to be ambiguous or ungradable rather than submitting them. What we’re looking for 1. Master’s or PhD in materials science, metallurgy, ceramics, polymer science, condensed matter physics, or a closely related field. 2. Principal, staff, or fellow-level experience in industrial or national lab research, or a faculty appointment at a leading university. 3. Deep technical depth in at least one materials subdomain, with hands-on ownership of the work. 4. Able to explain why an answer is correct clearly enough that another scientist could verify it. 5. Publications, patents, or a record of technical leadership is a strong plus. 6. AI or LLM evaluation experience is a plus, but not required. How the selection process works 1. Apply and complete the screening questions 2. Complete the AI interview for this role — it’s how your expertise gets in front of the client 3. Profile review 4. Onboarding — our Talent Ops team will get you set up

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