MercorDomain experts
Quantum Information Science Physicist (PhD)
up to $110/hr
About this role, in Mercor's words
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.
A closer look
More context, when you need it.
Full job descriptionThe original posting, in Mercor's words
Posted by Mercor, reproduced here so you can judge the role before clicking. Original posting ↗
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
• 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.
• We verify your papers and authorship against the public record.
• 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.
• 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.
Working with MercorPay, getting started and work, from Mercor's own pages
What the platform publishes in general. These are not extra terms confirmed for this particular role.
- Getting paid
- Hourly work is paid every Wednesday for the previous week, through Stripe Connect or Wise, whichever Mercor assigns to your region. Stripe holds the first payout for 7 days. talent.docs.mercor.com/how-to/payments, talent.docs.mercor.com/policies/contracts
- Before your first paid task
- An AI interview of about 20 minutes, reused for other roles that need it, then any assessments the role asks for. Before logging hours: ID verification through Persona, a tax form, the contract documents, the Insightful time tracker, and a background check the onboarding guide says usually takes one to two weeks. talent.docs.mercor.com/support/ai-interview, talent.docs.mercor.com/how-to/apply, talent.docs.mercor.com/policies/contracts, talent.docs.mercor.com/how-to/id-verification, talent.docs.mercor.com/working/project-onboarding
- How much work
- Hearing back after the AI interview usually takes 2 to 4 weeks. When a project ends you are not moved to a new one automatically. talent.docs.mercor.com/support/ai-interview, talent.docs.mercor.com/support/project
- Contract
- Independent contractor with Mercor.io Corporation of San Francisco, which deducts no taxes. talent.docs.mercor.com/policies/taxes-work-authorization, talent.docs.mercor.com/support/legal
Read on the pages linked above on 23 Sep 2026. Platforms change these terms, so check the linked page before you rely on one.
Alex's experience on MercorA personal account, not a promise about your project
Alexruns this site and has worked on Mercor himself. Written 31 Aug 2026.
- Every interview I took used an AI interviewer. They always checked my microphone and camera and asked me to share my screen, and some included a timed exercise of around 25 minutes.
- Many later applications needed no new interview from me. With the required assessments complete and no other outstanding steps, applying could be close to one click.
- The application I checked did not ask for a rate. My offer matched my minimum in Work Preferences. That does not establish that Mercor always caps higher offers at your preference.
- Onboarding and setup came to about 4 hours, with more security and tool setup. Of the three platforms I worked on, it was the most complicated for me. That is an estimate from my projects, not a platform average.
- Mercor required the Insightful tracker on my project. I observed app and website usage monitoring, and I am unsure which screenshot settings applied, so check the configuration.
- I was paid for hours logged, once every 14 days, and I have not experienced late or incorrect payments. That was my arrangement. Mercor's current documentation describes weekly payout cycles, so check your own offer.
One person's applications, not a promise about yours.
What happens after you apply, first-handHow does the pay compare?Across Domain experts roles, not this role alone
From our own daily reads of every open listing on the platforms we follow, not from Mercor's posting. How we count.
Pay across the field: Domain experts
Across 698 roles in this field with a stated hourly rate in US dollars, on 18 platforms, the middle half pays $65 to $130 an hour at the top of the range, and the median is $100. This role's top rate, $110, is in the middle half. Of the 260 distinct rates we count, 159 are lower and 93 are higher.
| Platform | Roles | Top of range, per hour |
|---|---|---|
| Alignerr | 149 | $24 to $200 · median $80 |
| micro1 | 136 | $28 to $400 · median $110 |
| AfterQuery | 88 | $45 to $175 · median $100 |
| Handshake AI | 79 | $30 to $300 · median $85 |
| Mercor (this platform) | 77 | $55 to $450 · median $120 |
Sources and listing historyWhere this information comes from
This listing's history
- First seen here on 25 Sep 2026, and still on Mercor's list when we last read it on 13 Oct 2026.
- The listed pay has not changed since then.
- Posted by Mercor
- First seen by AIPayList
- Last seen live
- Listing ID
- list_AAABoNekthWNOoiOk35BMpG5
Pay as the source states it: “80–110 USD HOUR”.
Platforms sometimes change a rate after posting. Every listing here is re-read at least once a day and corrected when its source moves, so a difference you see on the platform is usually a change made since the last read. How freshness works.
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