Math & STEM

If you have real mathematical training (a degree in progress, a master's, a PhD, or years of quantitative work) and you want a remote math job that pays you to use it from home, STEM, math and proofs is the part of the AI training economy where pay rises most steeply with your credential. The math and STEM roles open on this board today typically pay Unavailable to Unavailable an hour, with a median of Unavailable, and the very top of the market (frontier-lab proof verification, research-grade reasoning) sits at the top of the platforms' rate cards, such as $50–$90/hr typical, to $400 for micro1's STEM research scientists and $65–$175/hr for AfterQuery's math and STEM experts. That is not a get-rich-quick pitch. It is a real, if lumpy, freelance market where credentials and the ability to write a clean proof are worth more than in almost any other online gig. This guide explains exactly what the work is, who pays what, how to get in, and where the traps are, so you can decide whether it is worth your time.

What "STEM, math & proofs" AI training actually is

Large language models are famously unreliable at rigorous mathematics. They can produce a plausible-looking argument that is subtly (or catastrophically) wrong, skip steps, or "hallucinate" a lemma that does not exist. To fix this, AI labs pay humans who genuinely understand mathematics to do four broad things, and your day is usually some mix of them:

• Author hard problems. You write novel, high-difficulty math problems (and their fully worked, correct solutions) that stress-test a model. Outlier's math role (archived October 2025) describes it as "create and answer mathematical questions to train AI models".
• Write and grade reasoning traces / proofs. You produce step-by-step derivations (a "reasoning trace") that are logically airtight, or you read a model's proof and mark exactly where it goes wrong. This is the "proofs" part, and it is the most valuable because grading a proof correctly requires the same skill as writing one.
• Rank and compare model outputs. Given two or more AI answers to the same problem, you decide which is better and why. This is the human-preference data that sits at the heart of RLHF (reinforcement learning from human feedback): the policy model generates completions, a reward model scores them, and your judgments are what teach the reward model what "good" looks like.
• Build verifiers and RL environments. A newer, higher-paid category: you help build the automatic checks (a verifier, a unit test, a rubric) that let a model be trained with Reinforcement Learning with Verifiable Rewards (RLVR), where a programmatic check confirms correctness instead of a human doing it every time. Mercor builds these "realistic data-rich worlds" with "rigorous tasks and verifiers" and pays experts to design them.

Why is this so well paid compared to image tagging or chatbot rating? Because the supply is tiny. At Olympiad level, a single question can take a domain expert hours to annotate, and the labs cannot cheaply substitute this. A 2026 arXiv paper on math proof reward models exists precisely because proof verification is a bottleneck that money is being thrown at. That bottleneck is your leverage.

The kinds of math in demand

The category is broad. Real postings and platform pages ask for people across:

• Competition and Olympiad math (AMC/AIME/IMO-style problem design and grading).
• Undergraduate and graduate pure math (real and complex analysis, abstract algebra, topology, number theory) where clean proofs matter most.
• Applied math, statistics and probability (an expired NoDesk listing for a PhD Math & Statistics tutor via Mercor, at $110 to $130 an hour, shows the type, read 10 November 2026).
• Theoretical physics and quantitative fields treated as adjacent (many STEM roles bundle physics, chemistry and CS reasoning).
• Formal / machine-verified proofs (Lean, Coq) for the most advanced RL-environment work, which pays at the top of the range.

How much you actually get paid

Here is the honest picture. The typical band of the roles open today, Unavailable to Unavailable an hour, is real, but the number you personally hit depends on three things stacked on top of each other: your credentials, your platform, and your country. Domain expertise is, in the words of one platform's own breakdown, "the single biggest lever."

Pay by seniority / credential

This is the table that matters most. The first three rows are the tiers in Mercor's article on AI trainer pay across the industry (updated 23 June 2026, read 29 October 2026), its estimates for early 2026; the last row is this board's own figure, read live.

The distribution is skewed. Across all AI training work, the roles open on this board have a median of Unavailable an hour in a typical band of Unavailable to Unavailable, and Mercor's article puts the national average at $31 an hour, the median at $25-33, the upper quartile at $53-58 and only the top decile above $100 (its estimates for early 2026, read 29 October 2026). STEM and math specifically pull toward the top of that curve because the credential bar is higher, but you still have to qualify into it. Simply being on a platform does not put you at the top of the band; being a math PhD who passed the specialist assessment does.

Pay by platform

The eight platforms you asked about are not interchangeable. They differ in who they accept, how they pay, and what the ceiling is. Rates below are this site's researched rate card for each platform, read live, with the day each card was last checked against the platform's own pages; treat them as ranges, not promises.

A few things jump out of that table. Outlier and DataAnnotation are the most accessible and the most beginner-friendly for someone with a math degree, but their ceilings are lower than the credential-gated platforms'. Mercor, AfterQuery and Handshake AI are where the top-paid math work lives, but they gate hard on an actual PhD or equivalent. Terac is a "paid feedback" add-on, excellent per-task pay but not a source of steady hours. Each card opens its platform's pay page, which states the card and the sources it was checked against.

Pay by country (this is the big asterisk)

The same task pays wildly different amounts depending on where you live, because platforms use region-tiered pay and because some of the best platforms only accept a handful of countries at all. DataAnnotation publishes no country list, but worker reports point to six countries only (the US, Canada, UK, Ireland, Australia and New Zealand; its page here), while Outlier's working guidelines list 117 places, India and the Philippines among them (read 10 November 2026). Here is how many of the math and STEM roles open on the board today take people from each country:

If you are outside the top-tier countries, the realistic move is to target the credential-gated global platforms (Mercor, micro1, AfterQuery; Handshake AI's program asks for US-based people) where a verified PhD can still command a high global rate, rather than the US-only self-serve queues. A math PhD in any country is far more portable than a generalist labeler in any country.

The platforms in detail

DataAnnotation

The most common starting point for people with STEM degrees who are not (yet) PhDs. You take an unpaid starter assessment (testing writing, reading comprehension and logical reasoning), then unpaid domain qualification tests (Math, Coding, Chemistry, Physics, etc.). Pass the math qualification and you unlock STEM projects, which its rate card, checked 2 Nov 2026, puts at $40–$125+/hr. Payment is via PayPal. The real friction: qualification tests are unpaid, acceptance is selective, and some applicants report never hearing back.

Outlier

Outlier's math expert page is the clearest job description in the industry. Tasks (as archived in October 2025): "create and answer mathematical questions to train AI models," "evaluate and rank AI-generated responses for clarity and correctness," and give constructive feedback. Requirements: pursuing or holding a bachelor's or higher in math or a related subject, at least a year of professional experience in mathematical analysis, strong English writing. Its rate card, checked 8 Nov 2026, reads Up to $7–$50/hr; ML, EE and quant finance up to $150/hr. The catch, per Glassdoor's Outlier reviews and third-party reviews, is that math and coding sit at the top of Outlier's range while text tasks pay less, and task availability "fluctuates heavily." It is legit and pays through PayPal or AirTM (its archived math page), but do not count on 40 steady hours.

Mercor

Mercor is a "apply once, get matched" platform: you do an AI interview once, and it is reused across opportunities. Its whole model is connecting frontier labs with credentialed experts, more than 30,000 of them as of October 2025 (Contrary Research, updated 20 October 2026), for post-training, evaluation, rubric design and RL-environment construction. Its rate card, checked 8 Nov 2026, puts its science experts at $45–$200/hr and reads $63–$90/hr median listing; experts to $450/hr overall. Mercor is aggressively expanding its RL-environment capability (it acquired Sepal AI, as its counsel Orrick announced on 6 February 2026, and announced the Deeptune acquisition on 9 July 2026), which means more, not less, demand for people who can build verifiable math tasks. The honest caveat: pay only accrues when a project is live, and there are "no tasks" waiting periods.

AfterQuery

A Y Combinator-backed research lab that builds expert-generated training data and RL environments. Work arrives in project waves, typically 2-3 week assignments at 10-20 hours/week (aitrainer.work, updated 29 October 2026), followed by quiet queues, with weekly (Friday) payouts. Its rate card, checked 1 Nov 2026, reads $15–$175/hr; middle half $50–$104/hr, with its math and STEM experts at $65–$175/hr. For a math PhD who can commit in bursts, it is one of the highest ceilings available.

Handshake AI

Handshake's AI fellowship program recruits US-based students and graduates, with opportunities tagged from bachelor's to postdoc level, to evaluate AI responses and design hard prompts (read 29 October 2026). Its rate card, checked 8 Nov 2026, reads $15–$150/hr; physicians and surgeons to $300/hr, paid hourly, with the rate published upfront for each opportunity. It is well-suited to current grad students because it grew out of the university-careers platform.

Alignerr, micro1 and Terac

Alignerr (powered by Labelbox) has a rate card, checked 6 Nov 2026, of $10–$200/hr (German voice actors to $280/hr), with STEM and formal-proof math at $35–$200/hr, but the effective ceiling for non-specialists is lower, so it rewards getting onto a specialist track. micro1 uses an AI screener ("Zara"), and its card puts STEM research scientists at $50–$90/hr typical, to $400, making it a good fit for math people who also code. Terac is different in kind: an AI-native expert panel where you do studies (interviews, feedback sessions, consultations) and tasks, verified by a short voice conversation; its card reads $40–$350/hr expert; per task to $750; studies $1–$315. It is a strong supplement, not a full-time income.

Requirements, tools and skills

Credentials

There is a real ladder. At the bottom, a bachelor's in math or a hard STEM field (or current enrollment) is enough for Outlier and to attempt DataAnnotation's math qualification. In the middle, a master's widens access and lifts rates. At the top, a PhD in pure or applied math, statistics, or theoretical physics is what unlocks the top-paid work on Mercor, AfterQuery and Handshake AI. Some listings will accept a bachelor's plus long professional quantitative experience in lieu of an advanced degree.

Skills that actually get you paid more

• Writing clean, rigorous proofs and being able to grade them. This is the differentiator. Anyone can compute; being able to say precisely why step 4 is invalid is the paid skill.
• LaTeX. Nearly every serious math role requires it for typesetting solutions and traces. If you are rusty, fix that first; it is table stakes.
• Clear technical English writing. You are producing training data that must be unambiguous. Sloppy prose gets your work rejected regardless of whether the math is right.
• Following long, fussy instructions exactly. The most common reason good mathematicians fail these gigs is not the math, it is not reading the multi-page rubric carefully.
• Some coding (Python) and, at the frontier, formal methods (Lean/Coq). Not required everywhere, but they open the highest-paid RL-environment and verifier work.

Tools you will use

The models themselves (many platforms give free access to frontier models), a LaTeX editor, sometimes an in-house annotation interface, occasionally a formal-proof assistant, and standard freelance plumbing (PayPal, a time tracker such as Insightful on some platforms).

How to get started, step by step

1. Fix your LaTeX and pick a proof or two to warm up. Before you apply, be sure you can typeset a clean solution quickly. This is the single most common gating skill.
2. Apply to two or three platforms at once, matched to your credential. If you have a PhD, lead with Mercor, AfterQuery and Handshake AI. If you have a bachelor's or master's, start with Outlier and DataAnnotation. Diversify, because task availability on any one platform is spiky.
3. Treat the (unpaid) assessment like a real exam. DataAnnotation's own community consistently says the failure mode is rushing. Quality beats speed: read every instruction, do not skim, and do not take the test exhausted. Most of the people who fail the harder qualifications fail on carelessness, not competence.
4. Do the AI interview seriously (Mercor, micro1). It is reused across opportunities, so a strong interview pays off repeatedly. Speak to your specific math background and concrete examples of rigorous work.
5. On your first paid tasks, over-deliver on rigor and formatting. Your early accuracy determines whether you get routed to the higher-paying, higher-trust projects. This is where the real money is: not the base rate, but getting onto the specialist track.
6. Stack platforms and log your effective hourly. Track paid hours vs. total hours (including task-hunting and unpaid screening). Your effective rate can run 20 to 30 percent below your headline rate, per Breaking Even (updated 14 September 2026).

Realistic earnings

Set expectations honestly. The top of the rate cards is real but it applies to credentialed specialists on live, high-trust projects, and it is rarely 40 hours a week. A grounded picture:

• A math bachelor's on Outlier/DataAnnotation, once qualified, earns within those platforms' cards on the tasks they get (DataAnnotation's STEM projects: $40–$125+/hr), with availability that ebbs and flows. Many people treat it as a side income rather than a salary.
• A math master's or advanced grad student who gets onto specialist tracks can push past the median of the math and STEM roles open today (Unavailable an hour) toward the high end of their typical band (Unavailable) on good projects.
• A math PhD on Mercor/AfterQuery/Handshake AI can command the top of those platforms' cards while a project is live (Mercor's science experts: $45–$200/hr), but income is bursty (waves of a few weeks, then quiet). Someone stitching multiple platforms together can build this into a serious income, but it takes hustle and there are dry spells.

The annualized figures on salary sites are real for consistent, high-tier contributors, but they assume you stay busy. The most common disappointment in reviews is not low rates, it is inconsistent task availability and unpaid screening dragging down the effective hourly.

Payment and taxes

Almost every platform hires you as a 1099 independent contractor (US) or the local equivalent, not an employee. That means:

• No tax is withheld. You are responsible for your own. In the US, budget for the 15.3% self-employment tax ( IRS, read 29 October 2026) plus your income-tax bracket, and set aside part of what you earn.
• You will get a 1099 if you cross the threshold. For payments made in tax year 2026 the 1099-NEC reporting threshold rose from $600 to $2,000, adjusted for inflation from 2027 (IRS Publication 1099, read 29 October 2026), but you must report all income regardless of whether a form is issued. If you are paid via PayPal you may also see a 1099-K; do not double-count the same income across a 1099-NEC and a 1099-K.
• Payment methods and cadence vary. DataAnnotation pays via PayPal every few days; Outlier weekly via PayPal/AirTM/ACH; Mercor and AfterQuery weekly; Terac via wallet-to-bank, PayPal or gift card. Confirm the method before you invest hours.
• Track your expenses. As a contractor you can typically deduct legitimate business costs (a share of internet, a laptop, software). Keep records.

Pros and cons

Pros: genuinely high pay for the right credential, fully remote and flexible (no minimum hours on most platforms), intellectually real work (you are writing and grading actual mathematics, not tagging cat photos), and a direct on-ramp for grad students and academics tired of adjunct pay and grant writing. Free access to frontier models is a nice perk.

Cons: income is lumpy and unpredictable (waves and dry spells), unpaid assessments and task-hunting eat into your effective hourly, rates in 2026 have softened at the generalist end, best platforms are geo-restricted, and you carry all the self-employment tax and admin burden. There is also the risk of being silently deprioritized or flagged (some platforms auto-flag suspected AI-assisted submissions, which is ironic and occasionally catches honest workers).

Red flags and scams to avoid

The legitimate platforms in this guide never charge you to work. Money flows one direction only: from the company to you. Use these rules:

• Never pay an upfront fee. No "registration fee," "training fee," "background-check fee," "certification," or "starter kit." This is a scam: real employers do not charge you.
• Distrust unrealistic promises. "Expert pay for general AI training, no experience required" is bait. Real generalist work pays far less (the data labeling roles open on this board have a median of Unavailable an hour); the high rates require real credentials.
• Never accept payment by gift card, crypto, wire transfer, or a "deposit this check and forward part of it" arrangement. The last is the classic fake-check scam and ends with you owing the bank.
• Verify the domain and recruiter. Scammers clone real job posts and impersonate recruiters on LinkedIn. Apply through the platform's official site, not a link in an unsolicited DM.
• Be wary of a hire that skips all screening. Real STEM/math work always involves a qualification task or interview. An instant "you're hired, just pay to onboard" is a scam.

The 2026 outlook

Demand for exactly this skill is rising, not falling, and there is a nuance worth understanding. The overall AI data labeling market is about $2.32 billion in 2026 and forecast to reach $6.53 billion by 2031, a 22.95% CAGR (Mordor Intelligence, read 29 October 2026). Within that, Mordor notes that machine pre-labeling is compressing prices for routine text work, while specialized human experts (mathematicians, doctors, lawyers) are the part of the work that cannot be commoditized. Mindrift calls math AI training one of the highest-paying remote opportunities for mathematicians, with top rates of $90 an hour on its own projects (18 June 2026, read 10 November 2026).

The one real headwind for math specifically is RLVR. As labs get better at verifiable rewards, some routine "grade this answer" work gets automated by programmatic checkers. But that shift does not remove humans, it moves them up the value chain: instead of grading, experts are now paid to build the verifiers and RL environments, which is harder and better paid, and it is exactly why Mercor is acquiring RL-environment companies. Net-net for 2026 and into 2027: the floor for generic labeling is under pressure, but the ceiling for genuine mathematical expertise (proof writing, verifier design, research-grade reasoning) is holding up and arguably rising. If you can write and grade a rigorous proof, you are on the right side of this trend.

Bottom line

STEM, math and proofs is a durable corner of the AI training economy where pay rises steeply with your credential. If you have a math or hard-STEM degree, start with Outlier and DataAnnotation, take the assessments seriously, and build up your accuracy to reach specialist tracks. If you have a PhD, go straight for Mercor, AfterQuery and Handshake AI, where the top of the rate cards is real. Diversify across two or three platforms, track your effective hourly honestly, set aside part of every payment for taxes, and never pay anyone a fee to work. Done right, it is one of the few online gigs where deep expertise is actually rewarded at expert rates.