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The UX researcher whose findings outlive the project

$158,090estimated top of the range · middle $95,000 / yr
High AI exposure

UX Researchers in the United States earn a median of $95,000 a year. Pay starts near $58,000. The top of the range is estimated at $158,090. The Bureau of Labor Statistics does not publish a separate wage series for this exact title, so this figure is derived from the closest occupation it does track and is labelled an estimate.

Source: PayCrunch estimate. Last checked 9 September 2026.

Entry level
$58,000
Top-end estimate
$158,090
Education
Master's degree in HCI or Psychology
Lower disruption Higher exposure High AI exposure
Entry · $58,000 Top-end estimate · $158,090 Middle $95,000

Wages — PayCrunch estimate. The Bureau of Labor Statistics does not publish a separate wage series for UX Researcher; figures are derived from the closest occupation it does track and are labelled as estimates. AI-impact rating is PayCrunch's editorial assessment. Updated September 2026.

🆕 New & Trending AI Tools for UX ResearcherReviewed September 2026

We track new AI-tool launches every week and refresh this list — here’s what’s gaining traction for UX Researcher work right now.

Julius AINEWFree / $20 mo

AI data analyst that runs statistics and charts from plain-language prompts.

How an UX Researcher uses it: analyze datasets and generate figures without writing code

NotebookLMNEWFree / $7.99 mo

Google tool that answers questions grounded only in the documents you give it — with citations.

How an UX Researcher uses it: load your own manuals, policies, or PDFs and ask questions that stay accurate to the source

ElicitFree / $12 mo

AI research assistant that finds and summarizes papers.

How an UX Researcher uses it: run a literature review and extract findings across dozens of papers fast

ConsensusFree / $9 mo

AI search that answers questions from peer-reviewed research.

How an UX Researcher uses it: get evidence-backed answers with the studies behind them

SciSpaceFree / paid

AI that explains papers and helps with literature review.

How an UX Researcher uses it: decode dense papers and trace citations quickly

SciteFree / $20 mo

Shows whether other studies support or contradict a paper's claims (Smart Citations).

How an UX Researcher uses it: check if a finding is actually backed by the wider literature before you cite it

ChatGPTFree / $20 mo

The most-used AI assistant — writing, analysis, research, and images from a plain-language chat.

How an UX Researcher uses it: draft emails and documents, summarize long files, and get instant answers to on-the-job questions

ClaudeFree / $20 mo

AI assistant known for careful writing, long-document analysis, and coding.

How an UX Researcher uses it: analyze big reports or spreadsheets and turn messy notes into clean, finished writing

Google GeminiFree / $20 mo

Google's AI assistant, built into Gmail, Docs, and Search.

How an UX Researcher uses it: draft and reply inside Google Workspace and research without leaving the page

The study comes before the redraw

A UX researcher finds out how people actually use a product, or why they cannot, before the team spends a cycle redrawing it. You plan a study, recruit the right participants, sit with them in an interview or a usability session, and turn what you saw into a finding a product manager can act on. Designers may be in the room. Engineers may watch a clip. The job is still the study. If the team only wanted another set of screens, they would have hired a different role.

The week moves between planning and contact with real people. One day you write a discussion guide and line up participants. The next day you run sessions and take notes you will still understand on Friday. Later you cluster what you heard, separate a one-off complaint from a pattern, and present the pattern with the evidence attached. A good readout changes a roadmap. A weak readout restates opinions the team already held. Your reputation is the difference between those two meetings.

Settings vary. A product company may assign you to one app for a year. An agency may ask for a study that ends when the contract ends. A hospital, a bank, or a public agency may need research on a tool the public has to use, not a tool they chose for fun. The methods travel. The stakes change. Learn the domain enough to hear what participants mean, and resist pretending you are the user because you use a similar app at home.

Interviews, studies, and the readout

Interviews are conversations with a purpose. You recruit people who match the situation the team cares about, you follow a guide so each session covers the same ground, and you still leave room when a participant says something the guide did not anticipate. You listen more than you pitch. You do not rescue them when they struggle with a task, because the struggle is the finding. After several sessions, you can say what kept happening, who it happened to, and what the team might change.

Usability studies put a design, a prototype, or a live product in front of someone and watch the attempt. You set a task in ordinary language. You notice where people hesitate, where they pick the wrong control, and where the words on the screen mean something the team did not intend. A survey can widen the picture when you need many responses rather than a deep session. A diary study can follow a routine across days. Pick the study that matches the decision. A fancy method aimed at the wrong decision is just expensive theater.

Analysis is the part amateurs skip. Notes become themes. Themes need quotes or clips that a skeptic can check. You separate what people say they want from what they did in the task. You label how solid the pattern is: seen in many sessions, or seen once and worth another look. Then you recommend a direction in language a designer and a product manager can use on Monday. The recommendation is yours to argue. The decision may still belong to them. Say that out loud so the readout does not pretend to be a decree.

Recruiting is half the craft and the half that slips. The wrong participants produce a confident, useless study. Write who you need in concrete life situations, not in a vague persona name. Work with the panel, the customer list, or the community your company can actually reach. Track no-shows. Pay the incentive the company approved, on time. Participants remember a sloppy operation, and so does the teammate who has to explain why the study cannot support the decision it was meant to inform.

No licence, and a body of studies instead

The role carries no occupational licence. No state board issues a card that allows you to run an interview or a usability study. A degree in human-computer interaction, psychology, anthropology, information science, or a related field is a common path. So is a switch from design, market research, or customer support if you can show studies of your own. Employers are buying judgment about evidence. They are not buying a licence number, because none exists to give them.

What stands in for a credential is a small set of study writeups. Each one should name the decision the team faced, who you included, how you ran the study, what you found, and what changed afterward. If nothing changed, say what you learned about the organization's appetite for evidence. That honesty reads as seniority. A writeup that claims a total victory, with no collaborator and no constraint, reads as fiction. Name the designer, the product manager, and the recruiter who helped. Research is almost never a solo act, even when you facilitated every session.

Ethics sit in the method, not in a poster. Tell participants what the session is for. Do not hide a sales call inside a research interview. Protect recordings according to your company's rule. Decline a study that is built to bless a decision already announced, or reshape it until there is something real to learn. You will not win every one of those fights. You can refuse to dress a predetermined answer up as a finding. Teams that want a decorative researcher will eventually bore you. Teams that want to be wrong in private, before launch, are the ones worth staying with.

A study writeup that holds up

Decision, participants, method, finding, and what the team did next. If you facilitated and someone else designed the screen, say both. The page should let a stranger see your judgment, not a collage of sticky notes with no ending.

Teams that hire a researcher on purpose

Hiring loops start with the writeups, then a conversation about how you choose a method, then a presentation of one study to people who will interrupt you. They want to hear why you picked interviews instead of a survey, or the reverse. They want to hear a finding you later softened because more sessions changed the picture. They want to know how you talk to a designer who hoped for a different result. Bring one study you can tell without reading your slides line by line. If you need the slides to remember the point, you do not yet own the study.

Ask how research is used in that company, one point at a time. Who commissions a study? Do designers sit in sessions? Is there a researcher already, or would you be the first? What decision is waiting on evidence this season? A team that has never used research may still be a good bet if a leader is ready to change a plan. A team that says "we do research" and then describes only a satisfaction number on a dashboard may want a different skill. Match yourself to the work, not to the poster in the lobby.

Agencies and research consultancies hire for client communication and for speed without sloppiness. In-house teams hire for depth and for the patience to repeat a study when a product changes. Startups hire a generalist who can recruit, facilitate, and present. Larger companies may split recruiting, operations, and the researcher who frames the study. Say which of those you want. A portfolio of only student projects can open a junior door if you label them honestly. It will not carry a senior title. Label school work as school work.

References should include someone who used your findings and someone who saw you treat a participant with respect. Ask them to describe a study that altered a plan, and a moment you pushed back on a weak brief. Research that never disagrees with the loudest person in the room has collapsed into note-taking. The people who hire you have usually been burned by note-taking dressed up as insight. They are listening for a spine.

From a first study to a research practice

Early roles are associate or coordinator seats. You recruit, schedule, take notes, and run pieces of a guide a senior researcher designed. You learn how a session feels when it goes quiet, and how to write a note that still makes sense later. The next step is owning a study: the plan, the sessions, the analysis, and the readout. That is the promotion that matters. A nicer slide template does not create the promotion. A study the team can cite by name is the promotion.

Senior researchers own a product area across several studies. They know the users well enough to spot when a new request repeats an old finding. They coach other researchers on guides and on readouts. A research lead or manager hires, sets the slate of studies, and protects time so the practice does not collapse into a rushed usability session the day before launch. Some people prefer to stay individual contributors and become the person a company trusts with its hardest study. Both paths are real. Pick the one that matches whether you want other people's plans on your desk.

Movement between companies is common, and so is a shift from agency life to an in-house seat once you want a longer memory of one product. What stalls a career is a folder of studies with no decision attached, or a habit of hoarding findings in a report nobody opens. Every year, keep two stories you can tell: one where research changed the product, and one where it changed your mind. Those stories are the portfolio and the interview. Update them when the product ships, not three years later when you are tired and job hunting.

You will work beside designers every week in a healthy team. Their job is the experience people see and touch. Yours is the evidence about how that experience behaves with real people. Respect the difference when you talk about pay, scope, and credit. A joint project can support both portfolios if each person states their role. Blurring the roles in a writeup makes both of you look less trustworthy. The collaboration is a strength. The job titles stay distinct because the work is distinct.

Dollars that belong to this estimate

These figures are PayCrunch estimates. PayCrunch built them because the Bureau of Labor Statistics does not publish a separate wage series for this exact title. The early figure is $58,000. The estimated median sits at $95,000. The top of the estimate is $158,090. Moving from $58,000 to $95,000 covers $37,000. Moving from $95,000 to $158,090 covers $63,090. No state median is attached to any of these dollars. Use this estimate for a research role, and do not paste in a number from a design seat or from a neighboring title.

Read $58,000 as the early end, a comparison for an associate who is still running parts of someone else's study. Read $95,000 as the middle of the estimate, a stronger anchor once you own studies and can show decisions that moved. Read $158,090 as the top of the PayCrunch estimate, a figure to mention for a lead scope and only with that label. The $37,000 step from the early figure to the estimated median is the growth you can narrate with a body of studies. The further $63,090 up to the top is about scope: a practice, a slate of studies, other researchers, or a product area whose decisions depend on your evidence.

Say the source if a recruiter calls the number "market" without a definition. The Bureau of Labor Statistics does not publish a separate wage series for this exact title, so an honest comparison names the PayCrunch estimate and the company's band side by side. Ask whether the band is base pay. Ask where the band's middle sits relative to $95,000. A company can pay above the estimate or below it. What you want is a labeled comparison, not a borrowed wage from a different job that happens to share a hallway with research.

Findings and pay in the same conversation

Bring the same clarity to money that you bring to a readout. If the offer is near $58,000 and the seat is truly an associate role, with a senior researcher designing the studies, ask what changes when you own a study of your own, and mention the $37,000 distance to the estimated median of $95,000 as the map. If the offer is near $58,000 and they already expect you to frame studies, recruit, facilitate, and present alone, say that the estimate places that scope nearer the middle. Show the writeups. Let the work carry the sentence.

An offer near $95,000 is the middle of this estimate. Talk about what sits above it only if the job adds a real change: mentoring, a research roadmap, responsibility for a whole product area, or a lead title with people on it. The top of the estimate is $158,090, which is $63,090 above the estimated median. Keep that top in its own sentence. A senior title on a single junior-sized study queue falls short of the top of the estimate. Ask how many studies are in flight, who else researches, and who can stop a launch. Price the answers you actually hear.

There is still no licence to trade on. The studies are the proof. A short course can explain how you learned a method. It should not be the center of the offer talk. Keep the center on $58,000, $95,000, and $158,090, each with its label, plus the level the company is hiring. If a bonus or equity appears, ask for base pay as its own number so you can see whether base is near the early figure, near the estimated median, or reaching toward the top. A blended total is hard to compare, and researchers of all people should refuse a muddy metric.

Close by repeating the anchor and the reason. An associate path discussed against $58,000, with a review toward $95,000 once you own studies, is one clean story. A lead path discussed against $95,000, with $158,090 reserved as the top of the PayCrunch estimate, is another. The Bureau of Labor Statistics does not publish a separate wage series for this exact title, so you are naming an estimate on purpose. That is the right tone for this work. You are asking the company to be as precise about the offer as you are about a finding.

The top of UX Researcher pay — and how to get there with AI

$158,090top-end estimate for UX Researcher

PayCrunch estimate - derived from the closest occupation BLS tracks (Web and Digital Interface Designers, 15-1255). This figure is PayCrunch’s estimate, not a Bureau of Labor Statistics published wage for this exact title.

And the role it leads to — Software Developers — reaches $272,670 in California.

$58,000entry$95,000middle$158,090top end

What lifts a UX researcher toward the top of this range is not running more studies but making past findings findable, so product decisions are made against accumulated evidence instead of last quarter's memory.

Soliciting and integrating feedback from design and technical staff, evaluating competitive products, and giving quality assurance staff the specifications they test against produce a large amount of durable knowledge. Almost all of it dies in slide decks. Six months later somebody proposes a flow that was already tested and rejected, and nobody can find the evidence in time to say so. Building the store that fixes this is now realistic for one person: transcripts from Otter.ai, a consistent tagging scheme, and a question-answering layer over the archive with NotebookLM. The trap is treating it as a filing job. It is a research design job, because the tags decide what can be asked later.

Your playbook, by where you are now

Just startingMake one study reusable

  1. Write a study plan that states the decision it will inform, otherwise the results have nowhere to go.
  2. Record and transcribe sessions consistently, and keep participant consent and anonymisation strict from the first study.
  3. Separate observation from interpretation in your notes, because only the first stays true a year later.
  4. Store findings as short numbered statements with evidence attached rather than as a deck.
  5. Follow one recommendation of yours through to whatever shipped, and write down what happened.

What proves it: A study archived as evidence-backed statements a colleague can read cold.

Realistic span: your first eighteen months

A few years inGive the archive a shape

  1. Agree a tagging scheme with the designers who will search it, covering product area, user type, task and confidence.
  2. Backfill the last two years of studies into that scheme, which is dull work and the reason nobody has done it.
  3. Put the collection into NotebookLM so a question can be answered with citations, and verify every quotation against the original transcript.
  4. Track competitor evaluations in the same store so product comparisons stop restarting each time.
  5. Publish a monthly digest of what was learned and what it changed, aimed at engineers as much as designers.

What proves it: A repository other teams search without asking you first.

Realistic span: years two through five

ExperiencedRun research as an operation

  1. Maintain a participant panel with recruitment, screening and fair incentives so a study can start in days rather than weeks.
  2. Set the standard for what counts as evidence, and hold the line when a strong opinion arrives without any.
  3. Feed the repository into test specifications so quality assurance staff check the behaviours real users struggled with.
  4. Train product managers and designers to run their own small studies safely, and review what they produce.
  5. Weigh where this work is priced highest, since California product organisations fund research operations most heavily.

What proves it: A research operation with a panel, a repository and a documented evidence standard.

Realistic span: six years and beyond

The next 90 days

Spend the next three months turning your team's last two years of research into something searchable. Gather every report, transcript and note you can get access to, including studies run before you arrived. Reduce each one to numbered findings written as claims, each with the evidence and the date attached, and tag them with a scheme you agreed with two designers beforehand rather than invented alone. Confirm consent and anonymisation as you go. Then answer a live product question from the archive and show the citations. The first time a UX researcher can end an argument by producing evidence from eighteen months ago, the role stops being seen as a service that runs studies on request and starts being seen as the place the organisation keeps what it knows.

Wage figures: PayCrunch estimate. The playbook is PayCrunch editorial guidance, not a guarantee of pay or placement.

Careers related to UX Researcher

Similar pay, same field

Where this can lead

Every figure is the national median from the U.S. Bureau of Labor Statistics (OEWS) shown on that role’s own page.

Never used AI before? Start here (2 minutes).

Start by killing your synthesis backlog. Put your interviews into a research platform with AI built in — Dovetail or Marvin — to auto-transcribe, tag, and cluster themes, or use Otter.ai for transcripts you drop into Claude. Synthesis is the researcher's biggest time sink; automating the first pass frees you for the judgment that actually matters.

For everything else, use Claude or ChatGPT to draft screeners, discussion guides, and survey questions, and Maze for fast, AI-assisted usability testing and analysis. Get participant consent for AI processing and keep identifiable data in compliant, non-training tools — never paste sensitive recordings into a public chatbot.

The one rule, forever: Protect participants and evidence quality: never upload personally identifiable research data (recordings, names, sensitive quotes) to a consumer AI tool without consent and a compliant, non-training environment, and honor your consent and data-retention terms (GDPR, CCPA). Treat AI 'synthetic users' and AI-generated summaries as hypotheses, not evidence — validate insights against real participants, and watch for hallucinated or over-smoothed themes before anything drives a product decision.
The plays — exact steps, exact prompts

Do these in order. Each one is copy-paste ready. You do not need to know anything about AI going in.

1
Automate synthesis without losing the real insight
Why this pays: Faster, deeper synthesis lets you run more studies and deliver insight while it still matters — the responsiveness that makes you a strategic partner, not a bottleneck.
DovetailMarvinOtter.ai
1
In Dovetail or Marvin, auto-transcribe and let AI suggest tags and themes across sessions, then re-read the highlights yourself to find what the model flattened.
2
Cluster findings while separating strong evidence from noise.
Copy-paste this prompt
You are a senior UX research analyst. Here are [de-identified] transcripts and highlights from [N] interviews about [topic]. Cluster the findings into themes with representative quotes, rank them by how often and how strongly they appeared, and separately list surprises, contradictions, and anything only one participant said that could still be important. For each theme, note the evidence strength. Do not invent quotes; use only what's in the text.
AI over-smooths and can fabricate quotes — every quote and theme must trace to a real transcript before it reaches a readout.
3
Write the readout yourself, anchored to verified quotes and the actual strength of the evidence.
What you'll haveSynthesis in hours with the nuance intact — the speed and depth that make you indispensable to the roadmap.
2
Design sharper studies, screeners, and surveys
Why this pays: Better-designed studies get cleaner data and answer the real question — the rigor that earns trust and bigger, more strategic projects.
ChatGPTClaudeMaze
1
Use Claude or ChatGPT to draft discussion guides, screeners, and survey items, then edit hard for leading questions and bias, and program the test in Maze.
2
Draft an unbiased guide or survey and pressure-test it.
Copy-paste this prompt
Act as a UX research methodologist. I'm studying [research question] with [method: interviews / usability / survey] and [audience]. Draft a discussion guide (or survey) that avoids leading and double-barreled questions, sequences from broad to specific, and includes screener criteria to reach [target users] while screening out bad-fit or fraudulent participants. Flag any question that risks bias or won't yield actionable data, and tell me what this study can and cannot conclude.
AI writes fluent but leading questions — scrutinize every item for bias, and make sure the design actually answers the decision at hand.
3
Pilot with two users, fix what breaks, then field the study.
What you'll haveCleaner data and studies that answer the real question — the rigor that wins strategic work.
3
Turn behavioral and survey data into insight
Why this pays: Researchers who triangulate qualitative findings with analytics and statistics speak the language of product and executives — mixed-methods credibility that pays.
ChatGPT (Advanced Data Analysis)HotjarMaze
1
Pull product analytics and heatmaps from Hotjar and survey data, and use ChatGPT Advanced Data Analysis to explore patterns and run basic statistics, then interpret them alongside your qualitative findings.
2
Run the right analysis and avoid over-claiming.
Copy-paste this prompt
You are a mixed-methods researcher and statistician. Here is [de-identified] survey and behavioral data [describe columns]. Run appropriate analysis: for [comparison] use the right test (and tell me why), report effect size and confidence intervals not just p-values, check assumptions, and flag where the sample is too small or biased to conclude. Then suggest which findings to triangulate with my qualitative data. Show the steps.
AI picks tests and can misapply them — sanity-check the method, and never over-claim from a small or skewed sample.
3
Combine the quantitative signal with your qualitative insight into one decision-ready story.
What you'll haveMixed-methods insight leadership trusts — the credibility that moves you into strategic, better-paid research.
4
Build a living research repository and atomic insights
Why this pays: A searchable repository makes past research reusable and makes you the source of institutional knowledge — visibility and leverage that drive seniority.
DovetailNotion AIMarvin
1
Store every study's tagged insights in Dovetail (or Notion) as reusable, searchable atomic findings, and use AI search to answer new questions from old data.
2
Design the repository taxonomy and insight template.
Copy-paste this prompt
Help me design a research repository taxonomy for a [product area]. Propose a tagging scheme (personas, journey stages, product areas, insight types), a naming convention for atomic insights, a template for an insight (evidence, confidence, date, source study), and rules for when an old insight should be revalidated vs archived. Then show how I'd answer '[a typical stakeholder question]' from the repository.
A repository is only trustworthy if insights carry evidence and dates — enforce provenance so stale findings aren't reused as current fact.
3
Publish a monthly digest of insights; being the memory of the org builds strategic influence.
What you'll haveReusable institutional knowledge with your name on it — the leverage and visibility behind senior and lead roles.
5
Democratize research and scale your influence
Why this pays: The researcher who enables the whole team to do good research, and governs its quality, becomes a strategic leader — the ResearchOps route to top pay.
MazeDovetailClaude
1
Build guardrailed templates and AI-assisted workflows in Maze so PMs and designers can run simple, valid studies; you review the hard ones and set the standards.
2
Write a one-page research-quality guide for non-researchers.
Copy-paste this prompt
Act as a ResearchOps lead. Draft a lightweight research-quality guide for non-researchers (PMs, designers) at our company: when to do research vs not, which method fits which question, a checklist for unbiased questions and ethical consent practices, when they must involve a trained researcher, and how AI tools may and may not be used with participant data. Keep it to one page and practical.
Democratization without guardrails produces confident bad research — you set the standards and gate the high-stakes, sensitive studies.
3
Train the team and own the standards; scaling good research org-wide is what earns leadership comp.
What you'll haveA team that researches well under your standards — the ResearchOps leadership that pays at the top.
Your 12-month sequence to the top of the range

How the plays above stack into a path from median pay toward the $145,000 tier.

Month 1
Move synthesis into Dovetail or Marvin — auto-transcribe and tag, then verify every theme by hand.
Months 2-3
Sharpen study and survey design with AI drafting plus rigorous bias editing, and run studies faster.
Months 3-6
Add mixed methods — triangulate qualitative findings with analytics and basic statistics for decision-ready insight.
Months 6-12
Stand up a research repository with atomic insights and a monthly digest.
Year 2
Lead ResearchOps and democratization with quality guardrails — the strategic scope behind pay at the top of the range.
Next steps for an UX Researcher

Some links below are affiliate or partner links. PayCrunch may earn a commission if you enroll or subscribe through them, at no extra cost to you. Wage figures on this page still come from the Bureau of Labor Statistics, not from these programs.

UX Researcher work is specific enough that a stamped 'check out these courses' block would be noise. BLS files this work as Web and Digital Interface Designers (SOC 15-1255). O*NET Job Zone 4 is typical: a bachelor's degree, so the honest next credential is a professional certificate or bachelor's-level coursework — not a random catalog dump.

The occupation's listed knowledge areas include Design and Psychology; the links search those subjects, not a generic 'career courses' list.

UX Researchers in this dataset list AJAX among the tools in use, so a program that names that stack is a better fit than a survey course.

Design programs on Coursera for UX Researcher work

Coursera search for design — a professional certificate or bachelor's-level coursework that lines up with computing, not a generic professional-development aisle.

Design courses on edX

edX search for design, aimed at computing (SOC 15-1255). Same field as the Coursera link, different university catalog.

Screened remote and flexible UX Researcher listings on FlexJobs

FlexJobs screens remote, hybrid, freelance, and flexible listings so you are not wading through unverified ads. This is a job-board search for UX Researcher work, not a claim that they list a counted SOC 15-1255 inventory.

Build an UX Researcher resume on Resume Now

Write an UX Researcher resume, or one aimed at Software Developers, instead of a blank template. Resume Now is a resume builder; we are not claiming a counted template set for this SOC.

Build an UX Researcher resume on Zety

An UX Researcher resume that names the actual tasks on this page, or the step-up title Software Developers, beats a blank template when you apply.

What UX Researchers earn by state

This page does not show a state table, and the reason is worth stating: the Bureau of Labor Statistics does not publish a separate wage series for this job title, so there are no official state figures to show. Scaling the national median by a cost-of-living index would produce a number for every state, but it would be an estimate of living costs wearing a wage’s clothes, and PayCrunch would rather show you nothing than that.

What the national figures say: pay starts near $58,000, the median is $95,000, and the top of the range is $158,090. Those national figures are a PayCrunch estimate, not a Bureau of Labor Statistics published wage for this exact title.

If you want to see how far state pay can move for jobs the Bureau does publish state-by-state, the best-paying state for every occupation is a free open dataset, and the salary-by-state statistics page summarises the pattern across all 824 of them.

Free data. Use any of it.

PayCrunch publishes verified, BLS-sourced salary + AI-playbook data on 1,000+ professions — free, no signup.

Frequently asked
Will AI replace UX researchers?
This is the honest worry — AI automates transcription, tagging, first-pass synthesis, and even 'synthetic users,' and it does put junior, execution-only research roles at risk. What it can't do is decide which question matters, earn a participant's honest answer, judge weak evidence, or move an executive. Researchers who climb to strategy and use AI to erase the busywork are safer than ever; those who only take notes and tag are the most exposed.
Can I trust AI synthesis or synthetic users?
No, not as evidence. AI over-smooths themes, invents quotes, and synthetic users are plausible guesses, not real behavior. Use them to speed a first pass or form a hypothesis, then validate against real transcripts and real participants before anything drives a decision.
Is it safe to put recordings and quotes into AI tools?
Only with consent and a compliant, non-training tool. Never paste identifiable recordings, names, or sensitive quotes into a public chatbot. Get participant consent for AI processing and honor GDPR, CCPA, and your retention terms.
How does AI raise my pay?
By freeing your time from ops (notes, tagging, recruiting logistics) for the high-judgment work leadership pays for — strategy, mixed methods, and influence — and by letting you run more studies. Strategic, high-rigor researchers who scale via AI reach the senior and staff band; execution-only ones stall.
What should I learn first?
An AI-enabled research repository (Dovetail or Marvin) to kill your synthesis backlog — it's the biggest time sink — then AI-assisted survey and usability tools (Maze), and enough statistics (via ChatGPT Advanced Data Analysis) to go mixed-methods.
Methodology & sources
  • Salary (median, 10th, top of the range) — U.S. Bureau of Labor Statistics, OEWS.
  • By state — the Bureau of Labor Statistics’ own state medians, limited to states employing at least 500 people in the occupation. No cost-of-living arithmetic is applied to a wage anywhere on this page.
  • The plays — PayCrunch's own step-by-step guidance using publicly available AI tools. Tool names/URLs are real and current as of August 2026; prompts written to work as-is. Verify any professional output before relying on it.

Sources