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Mercor

Chemistry Expert

MercorSTEM

$65-$105/hr

Worldwide

About this role

The research teams behind the best-known AI models come to Mercor for chemistry 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 chemists 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 chemists in the network to projects like: - Writing a synthesis or characterization problem from your own work, with the substrate, the conditions, and the analytical data, plus the reference answer a model's attempt gets graded against - Grading model-proposed routes and mechanisms: whether the disconnection is sensible, whether the selectivity claim is supported, whether the spectra match the structure assigned - Flagging where a model proposes something that works on paper and fails in the flask, given side reactivity, workup losses, or what the reagent tolerates Past rates have run $65 to $105 an hour, set per project by scope and depth. What we're looking for - A graduate degree in chemistry or a related field, with laboratory or computational 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 ran the reactions and interpreted the data 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 chemistry pool. When a project needs chemistry 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.

About the work CritPt is a public benchmark of research-level physics challenges, built to test whether frontier AI models can carry out genuine physics research reasoning rather than textbook problem solving. The benchmark paper is arXiv:2509.26574 and we recommend reading it before applying. It will tell you quickly whether this work interests you. We are engaging physicists to work on research-level physics problems in their own subfield. Depending on where your publication record fits, that can mean creating problems, solving them, reviewing completed work, or auditing it. We agree the specific assignment with you once you are matched to an area. This is research-grade work rather than volume work. Whatever you produce has to be complete enough for another specialist in your subfield to follow and verify independently, so written reasoning is part of every assignment. Research areas in this panel Twelve areas. We match narrowly: you need to have published on one of these specific phenomena, not in quantum information broadly. Each area lists the methods it requires. 1. Distributed quantum sensing and metrology with GHZ probes under noise: Distributed quantum sensing, quantum Fisher information, Greenberger-Horne-Zeilinger entangled probe states, GHZ-diagonal noisy states, Lindblad master equations, multiparameter phase estimation, entanglement distribution in quantum networks. 2. Quantum optics: two-mode squeezing, SU(1,1) interferometry, homodyne detection with loss: Optical parametric amplification, two-mode squeezed vacuum, two-photon formalism for sideband modes, Bogoliubov transformation of field operators, SU(1,1) nonlinear interferometry, cascaded phase-sensitive amplification with intermediate loss, balanced homodyne detection of quadrature sidebands, beam-splitter model of optical loss and detection inefficiency. 3. Quantum Shannon theory: Holevo and accessible information, classical capacity: Holevo information, accessible information of quantum ensembles, classical capacity of quantum channels, classical-quantum states, von Neumann entropy, optimality conditions for information-maximizing ensembles, convex optimization over probability distributions. 4. Holographic codes and random tensor networks, Haar averaging over O(N): Non-isometric holographic codes, Haar averaging over the orthogonal group, Weingarten calculus, random tensor network models of holography, moments of random linear maps, post-selection onto fiducial states, black hole interior reconstruction. 5. Matrix product states, sequential generation, holographic quantum simulation: Matrix product states, finitely correlated states, sequential generation of matrix product states, isometric tensor network circuits, transfer-matrix formalism for correlation functions, exponential decay of correlations, holographic quantum simulation, bond dimension and entanglement structure. 6. Eigenstate-to-Hamiltonian construction, commutant algebras, integrals of motion: Eigenstate-to-Hamiltonian construction, symmetric Hamiltonian construction from integrals of motion, quantum covariance matrix method, commutant algebras of local Hamiltonian families, Pauli string operator bases, Jordan-Wigner transformation, exact diagonalization of spin chains. 7. Quantum-limited imaging and superresolution: QFI, spatial-mode demultiplexing: Quantum Fisher information, quantum Cramer-Rao bound, Sudarshan-Glauber P representation, mutual coherence matrix of thermal sources, point spread function and diffraction-limited imaging, weak-source single-photon regime, spatial-mode demultiplexing, direct imaging and the Rayleigh resolution limit. 8. Quantum walks and spatial search on graphs, degenerate perturbation theory: Continuous-time quantum walk, spatial search on graphs, graph adjacency matrix spectra, simplex of complete graphs and truncated simplex lattices, degenerate perturbation theory, symmetry-reduced invariant subspaces, critical jumping rate and avoided level crossing. 9. Quantum foundations: higher-order interference, Sorkin parameter, GPTs as information resource: Higher-order interference, Sorkin parameter, multipath single-particle interferometry, spatial superposition as an information resource, generalized probabilistic theories, classical correlation polytopes, fingerprinting inequality, single-particle multiple access channels. 10. Quantum foundations: generalized noncontextuality, KCBS, ontological models: Generalized noncontextuality, Kochen-Specker contextuality, KCBS inequality, noncontextual ontological models, generalized probabilistic theories, simplex embedding, linear programming for nonclassicality, unsharp measurements and POVMs, white-noise robustness. 11. Quantum Shannon theory: private states, quantum and private capacity, superactivation: Private states and private bits, Choi-Jamiolkowski channel-state duality, quantum capacity and coherent information, private capacity of quantum channels, Werner states and symmetric subspace projections, degradable and antidegradable channels, superadditivity and superactivation of quantum capacity, spin alignment conjecture. 12. Quantum relative entropy, strong data processing, contraction coefficients of qubit channels: Quantum relative entropy, data processing inequality for relative entropy, contraction coefficients of quantum channels, strong data processing inequalities, qubit amplitude damping channel, monotone Riemannian metrics and the Bogoliubov-Kubo-Mori metric, Bloch-sphere representation of qubit channels, trace-distance contraction bounds. Methods we expect to find in your own publications You should be able to point to your own papers demonstrating at least one of the following families: - Entropic and information-theoretic: von Neumann entropy, Holevo bound, coherent information, contraction coefficients, Choi-Jamiolkowski duality - Open quantum systems: Lindblad master equations, quantum channels, degradable and antidegradable channels, amplitude damping and dephasing models - Random matrix and Haar integration: Weingarten calculus, moments of random linear maps, Haar averaging over the orthogonal group - Tensor network: matrix product states, transfer-matrix formalism, isometric tensor network circuits, exact diagonalization of spin chains - Convex and geometric: convex optimization over probability distributions, simplex embedding, correlation polytopes, linear programming for nonclassicality - Estimation theory: quantum Fisher information, quantum Cramer-Rao bounds, multiparameter phase estimation Who we are looking for A PhD in quantum information, quantum optics or a closely related field. This is a hard requirement. Postdoctoral researchers, research scientists and junior faculty are the strongest fit. Senior PhD students with a strong first-author record are welcome to apply. Published work on the specific phenomenon above, not the adjacent one. This is the single most common reason we decline otherwise excellent physicists. Having applied the information-theoretic toolkit elsewhere is not enough if your papers are not on the phenomenon in question. A verifiable publication record. Three to five representative papers with arXiv IDs or DOIs, ideally from the last five years. First author strongly preferred. Every paper you list will be checked against the public record. Working proficiency with LaTeX, Python, SymPy and Jupyter. Some familiarity with an agentic coding extension in VS Code is useful. Gaps here are acceptable if you declare them honestly. English at B2 or above, including written reasoning. A large part of the value you add is how clearly you set out your argument. Application steps 1. Apply and complete the attached form. Basic information, education, research experience, your method self-attestation, and up to five of the areas above that you are the best fit for. For each area you select, give an arXiv ID or DOI of your own paper as proof, with your author position and the methods it demonstrates. A selection without proof is not scored. 2. We verify your papers and authorship against the public record. 3. Then one of two things happens. Either we onboard you directly, or we invite you to a short live alignment call to agree the area and the assignment with you. 4. A brief 30 to 45 minute assessment may be added, but only where we need it. Most applicants will not see one. Commitment and rate 10 hours per week, sustained across an 8 to 10 week window, starting immediately. Remote and asynchronous with no fixed hours. $80 to $110 per hour, set by depth of subdomain match.

SME

KiCad Specialist

SMESTEM / Software, AI and Data

Up to $20/hr

Worldwide

In this hourly, remote contractor role, you will work as a Kannada Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across Kannada AI training projects. You will review AI-generated Kannada 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 accuracy, fluency, grammar, spelling, tone, cultural appropriateness, meaning preservation, instruction-following, formatting, 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 Kannada and English skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote 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 Kannada quality leadership will directly help improve the world’s premier AI models by ensuring that Kannada training data is natural, accurate, culturally appropriate, well-documented, and aligned with client expectations. Requirements - Bachelor’s or Master’s degree in Mathematics - Native or near-native Kannada proficiency with strong reading and writing skills. - Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear feedback in English. - 3+ years of professional experience in Kannada writing, editing, translation, localization, content QA, AI training, education, annotation, or related language-review workflows. - Strong understanding of Kannada grammar, spelling conventions, punctuation, tone, register, regional variation, and cultural context. - Ability to evaluate Kannada content against detailed rubrics and identify issues such as mistranslation, literal phrasing, unnatural tone, hallucinated claims, ambiguity, or inconsistent terminology. - Experience leading or supporting remote teams of trainers, annotators, reviewers, editors, 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, honeypots, and other quality documentation. - Experience with AI training, data annotation, large language models, prompt/response evaluation, or rubric-based LLM QA is a strong plus. Responsibilities - Quality monitoring: Spot-check Kannada items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues. - Trainer and QA communication: Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, and quality expectations. - Question handling: Respond to trainer/QA questions clearly and promptly, especially around Kannada wording, register, translation fidelity, cultural context, regional variation, and edge cases. - 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 Kannada project documentation, including style guides, trackers, FAQs, quality notes, examples, honeypots, 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 Kannada-specific style requirements. - Quality alignment: Ensure all trainers and QAs apply Kannada language guidelines consistently and understand updates as projects evolve. - Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for Kannada-language projects. Skills - LLM Evaluation - Translation Quality Review - Kannada - Kannada Quality Assurance - Style Guides - AI Training - Documentation - Trainer Feedback - English - Localization QA Additional skills - Kannada - Kannada Writing - Kannada Editing - Kannada Proofreading - English Proficiency - Translation - Localization - Transcreation - Content Review - Linguistic QA - Quality Assurance - Style Guide Development - Glossary Management - Terminology Management - Rubric-Based Evaluation - Error Analysis - AI Training Data - Data Annotation - LLM Evaluation - Prompt Evaluation - Content Moderation - Team Leadership - Remote Team Management - Training & Onboarding - Process Documentation - Google Sheets - Google Docs - Project Management - Dashboard Reporting - Discord

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