The physicist everyone books when the code has to work
$296,740top of the range in California · middle $172,250 / yr
AI is transforming this role
Physicists in the United States earn a median of $172,250 a year. Pay starts near $82,110. Pay reaches $296,740 at the top of the range in California, the best-paying state for this work among those with at least 500 people in the job.
Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025 (Physicists, SOC 19-2012). Last checked 9 September 2026.
Entry level
$82,110
Top of the range · California
$296,740
Education
Doctoral degree in Physics
Wages — U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025 (Physicists). Top of the range is the highest state-level figure among states with at least 500 people in the job. AI-impact rating is PayCrunch's editorial assessment. Updated September 2026.
🆕 New & Trending AI Tools for PhysicistReviewed September 2026
We track new AI-tool launches every week and refresh this list — here’s what’s gaining traction for Physicist work right now.
Julius AINEWFree / $20 mo
AI data analyst that runs statistics and charts from plain-language prompts.
How a Physicist 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 a Physicist 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 a Physicist 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 a Physicist uses it: get evidence-backed answers with the studies behind them
SciSpaceFree / paid
AI that explains papers and helps with literature review.
How a Physicist 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 a Physicist 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 a Physicist 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 a Physicist 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 a Physicist uses it: draft and reply inside Google Workspace and research without leaving the page
A research problem, or an applied one
A physicist spends the day on a problem that can be stated, tested, and argued. In a research group that means a question about matter, light, materials, the atmosphere, or an instrument that does not yet work well enough. You read what others have already shown, you design the next measurement or the next calculation, and you sit with results that are messier than the talk you hoped to give. In applied work the problem arrives from a product, a process, or a measurement someone needs by a date. The temperament is similar. The calendar is not.
The civilian version of this career is what belongs here: laboratories, companies, universities, and government groups that want research or applied science. Condensed matter, optics, biophysics, astrophysics, climate-related measurement, semiconductors, and instrumentation are ordinary homes for the title. You might build a model, run an experiment your lab already knows how to run, or turn a noisy signal into a number a colleague can use. You write it down so someone who was not in the room can follow you. Weapons design is outside this description. If a posting is really about that, it is a different job from the one these paragraphs cover.
A research day has more silence than outsiders expect, and more meetings than students expect. You debug a setup. You argue with a plot. You teach a student what the plot can and cannot support. You review a draft. An applied day may replace the draft with a design review: an engineer needs to know whether a sensor will hold, whether a material will behave, or whether the uncertainty is small enough for the product. In both rooms the useful person is the one who can say "this is solid" and "this is not ready" with the same calm.
Tools change by subfield. Some people live in code and large data sets. Some live at a bench with lasers or cryogenics or a fabrication step their lab owns. Some live in a mix. Nobody needs you to pretend every subfield is yours. Hiring managers trust a physicist who can go deep on one problem and still explain it to a non-specialist. They distrust a tour of jargon that never lands on a result. The result, and the limit on the result, is the product.
Why a doctorate shows up on the posting
Independent research postings usually expect a doctorate. A PhD in physics, applied physics, or a closely related field is the common preparation. The degree proves you carried a project from a fuzzy aim to a defended result, under people who know the field. A master's degree can open applied and industry roles where the work is real and the independence is narrower. A bachelor's degree sometimes leads to technical roles adjacent to physics. Those roles can be good jobs. They are not the same seat as a staff scientist who sets the aim.
You prepare by doing the degree honestly: coursework that gives you the tools, a group where you learn how evidence is kept, and a project you can explain without hiding. Publications help when they exist. A clear story about a failed measurement helps too, if you can say what you changed. Teaching experience helps on a faculty path and is less central in a company lab. There is no national licence that grants the title physicist. Your authority is the degree, the work, and the employer's trust. If a particular applied job later asks for a professional engineering credential or a security process, that is an extra requirement of that employer, not a universal rule of the profession.
Choose the group with your eyes open. A famous name does not guarantee you will learn to finish things. A smaller group with a problem you care about, and an advisor or a manager who reads your drafts, can be the better training. When you leave, you should be able to point at a result and say what was yours. That sentence is what the next employer is buying. The diploma is the proof you were allowed to attempt it.
Labs, companies, and public research groups
Universities hire physicists to teach and to run research programs. Companies hire them to solve measurement and materials problems inside a product. Government laboratories and research institutes hire them for programs that are larger than one faculty grant. Startups hire them when a device depends on physics the founders do not want to guess about. The posting may say scientist, physicist, or member of technical staff. Read the duties. If the work is a physical problem with a standard of evidence, you are in the right family.
In a hiring talk, walk through one project from the aim to the result, including the part that surprised you. Say what you did with your own hands or your own code, and what collaborators owned. Bring the names of people who will take a call. If you need a visa, a move, or time to finish a dissertation, say so before the group imagines you at a beamline next month. Industry often decides faster than a faculty search. A faculty search often wants a research plan and evidence you can attract students and funding. Match the story to the clock.
Bring a figure or a plot you can explain without slides if you have to. Hiring groups remember the person who can sketch the result, name the uncertainty, and stop talking when the point is made. They forget the person who needed a deck to recall their own work. If the role involves a shared instrument or a shared code base, say how you handled the days when someone else's change broke your measurement. Collaboration is part of the job, and a solo genius story that blames every delay on other people is a warning.
Ask what the seat really is. Will you have a project of your own, or will you support someone else's indefinitely. Will you publish. Who else is in the group when a measurement fails. What the first year is supposed to show. A beautiful campus or a famous product line does not answer those. The physicists who thrive can tell, early, whether they are being hired to think or hired to occupy a chair in a meeting. You want the first, even when the second is better paid for a while.
After the degree: supervised work, then a staff role
A postdoctoral appointment is common after the doctorate, especially if you want a university career. You join a group, you produce results on a faster clock than the dissertation, and you learn whether you want to build a lab of your own. Industry sometimes hires new doctorates directly and trains them inside a product line. National and government labs hire both new doctorates and people a few years past the degree. None of these is a moral ranking. They teach different skills: grants and students, or shipping a decision, or a long program with serious equipment.
Mid-career, the job often shifts from taking every measurement yourself to framing the problem and reviewing other people's work. Titles vary: staff scientist, senior scientist, associate professor, principal investigator. The substance is that other people now depend on your judgment about what is true. You spend more time on aims, on which experiment is worth the time, and on explaining uncertainty to people who must act. You still need to be close enough to the data to notice a weak figure.
Later forks include a broader technical leadership role, a deep specialist reputation, or a move toward management of a laboratory. Some people leave research and miss the problem. Some people stay individual contributors on purpose. When you compare offers, compare the scientific scope and the support: equipment, collaborators, the right to publish, and whether "scientist" means a real project. Pay is part of the comparison. So is the chance to keep doing physics rather than only reporting on it.
Entry, median, and two California figures
The figures come from Occupational Employment and Wage Statistics, May 2025, for Physicists. Research pay on the physicist series opens at $82,110. The national median of $172,250 sits $90,140 above that entry. That spread is large. Postdoctoral and early-career pay can sit nearer the entry figure. A staff role with a finished doctorate and a real project should look hard at any offer that treats $82,110 as the center of the market when the national median is $172,250. Use the median as the national anchor once you are past training.
California holds both the high end of the published range and the highest median, and they are different statistics. The high end of the published range in California is $296,740. The California median is $207,490. The high end differs from the median. The gap from the national median to that high end is $124,490. The gap from the national median to California's median is $35,240. A recruiter who says California pays $296,740 is quoting a range top. Typical pay in the state, on this chart, is $207,490. Those sentences should not be blended into one boast.
Read the other medians from the lower of this group toward California. New Mexico's median is $165,340. Maryland's median is $174,390. New York's median is $181,970. Texas's median is $184,040. California's median, $207,490, comes last and is the highest. Each of those is a median. None of them is the $296,740 range top. The lowest median in the published set is Michigan, at $94,520. The gap between California's median and Michigan's median is $112,970. Place changes the chart. It does not let you carry a range top into a state whose median is somewhere else.
An offer that keeps the labels straight
Write the base beside $172,250. If the job is in New Mexico, also write $165,340. Maryland belongs beside $174,390. New York belongs beside $181,970. Texas belongs beside $184,040. A California offer needs two labels: median $207,490 and high end of the published range $296,740. The $90,140 gap from entry to the national median is the spread you can mention when a staff seat is still priced like a training role. The $35,240 gap from the national median to California's median is a location fact for California only. The $124,490 climb to the California high end is a range top, and it differs from every median on this page, including California's own.
Equity, summer salary on a grant, and a bonus plan may sit outside the Bureau figures. Ask what is base. A faculty offer with a low academic-year base and a real startup package is a different shape from an industry salary near the median. Compare them as shapes, not as a single brag. If the base is already near a high state median, negotiate the work: your project, the people around it, and whether you can publish. Michigan's $94,520 is the low median, $112,970 under California's median, and it should not be used to anchor an offer in Texas or New York.
You are negotiating as a physicist whose preparation is usually a doctorate and whose work is research or applied science, not weapons design. Keep that identity next to the number. Name the statistic. A New Mexico median of $165,340 and a California range top of $296,740 are both public, and they serve different purposes only if you refuse to let them collapse. Use the median that matches the job, use $172,250 when the state is not on this short list, and decide with the science in view as well as the rate.
The top of Physicist pay — and how to get there with AI
$296,740what Physicist pay reaches in California
Highest state-level top-of-range annual wage for Physicists, among states with at least 500 people in the job. U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025.
$82,110entry$172,250middle$296,740top end
Inside a research group, the pay difference belongs to the physicist whose computational work other people depend on, not to the one with the most elegant private analysis.
Designing computer simulations to model physical data, performing complex calculations to analyse and evaluate results, and detecting and measuring physical phenomena in noisy data are increasingly done with assistance from code-writing models. Most groups adopt them badly: nobody checks the generated numerics, nobody keeps the prompts, and nobody writes down what failed. A physicist who takes that on, and who can teach it to postdocs and students who already have to be taught physics anyway, gains a form of standing that shows up in national-lab and industry offers rather than only in citation counts.
Your playbook, by where you are now
Just startingMake your own analysis reproducible
Put every analysis in Git from the first commit, including the failed branches, so a result can be traced back to the code that produced it.
Write one simulation with GitHub Copilot or Cursor helping, then independently verify it against an analytic limit you can compute by hand.
Learn enough C or C++ that you can read what a model produces for performance-critical numerics rather than trusting it.
Keep a running file of prompts that worked and outputs that were subtly wrong; the second list is the more useful one.
Get comfortable on Linux and with a cluster scheduler early, since compute access is a bottleneck long before ideas are.
What proves it: A repository where a colleague can reproduce one of your published figures from scratch.
Realistic span: graduate years and first postdoc
A few years inTeach it before anyone asks you to
Run a two-hour session for your group on verifying model-generated analysis code, and record what people got wrong.
Write the group's short guide: how to use assistance on a proposal draft, what must never be generated, and how results are checked.
Use NotebookLM against your own corpus of papers and internal notes when preparing lectures or a review talk.
Take over one piece of shared infrastructure, a fitting pipeline or a simulation harness, and make it something others can run without you.
Practise describing observations and conclusions in mathematical terms in front of an audience that is not expert; funding depends on it.
What proves it: A group-wide practice guide with your name on it that new members are handed.
Realistic span: years three through seven
ExperiencedOwn the method the field borrows
Publish the methodology, not only the physics result; a well-documented simulation approach is cited by people who will later hire you.
Build the training into research proposals as a funded component rather than as unpaid service.
Use Ansible software and Amazon Web Services AWS software to make your pipeline runnable outside your own institution.
Compare California laboratory and industry positions against academic offers, since the same computational skill is priced very differently in each.
Teach physics to students in a way that includes computational verification, so the habit propagates instead of dying with your cohort.
What proves it: An invited methods talk or workshop that other institutions asked for.
Realistic span: years eight and beyond
The next 90 days
Take one analysis you already trust and rebuild it in the open over the next ninety days: version-controlled in Git, with the model-assisted parts marked, and with at least one independent check, an analytic limit, a synthetic dataset, or a second implementation. Then present it to your group as a walkthrough of how you verified it, not as a physics result. That single session will tell you how badly your colleagues need this, and it is usually the beginning of being asked to run it for everyone.
Wage figures: BLS OEWS, May 2025. The playbook is PayCrunch editorial guidance, not a guarantee of pay or placement.
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).
Open an AI coding assistant — GitHub Copilot, Cursor, or Claude Code — and point it at your analysis code. Physics pay increasingly tracks computational skill, and an AI pair-programmer lets you write simulations, fit data, and refactor pipelines several times faster. Start by having it explain and speed up a script you already trust, so you can verify it does exactly what you expect.
For research, use free or low-cost tools: Elicit and Consensus to mine papers, NotebookLM to interrogate a stack of PDFs, Wolfram Alpha and Mathematica for symbolic checks, and ChatGPT or Claude for derivations you then verify by hand. Keep any export-controlled or proprietary work off consumer tools.
The one rule, forever: Treat every AI output as a hypothesis to verify, not a result. LLMs hallucinate derivations, physical constants, citations, and code that runs but is wrong — check the math, reproduce the number, and confirm every reference against the primary source before it enters a paper, proposal, or model. Never put export-controlled, ITAR, proprietary, or classified data into a consumer AI tool (a real constraint at national labs and defense contractors), and in quantitative finance never enter material non-public information. Reproducibility and research integrity are the standard.
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
Write simulations and analysis at AI speed
Why this pays: Computational throughput is the skill industry and labs pay most for. An AI pair-programmer multiplies how much modeling and data analysis you ship — the productivity that earns senior and industry-level comp.
GitHub CopilotCursorClaude Code
1
Use GitHub Copilot or Cursor to write and refactor Python/Julia — Monte Carlo sims, ODE/PDE solvers, fitting routines — reviewing every block before you trust it.
2
Generate a tested, vectorized simulation.
Copy-paste this prompt
Write vectorized, well-documented Python (NumPy/SciPy) to simulate [a damped driven pendulum / the 2D Ising model] with parameters [list them]. Include a convergence check, unit tests for known limiting cases, and a plot. Explain the numerical method and its stability limits.
Verify against analytic limits and known results before using output — code that runs is not code that's correct.
3
Have the AI write tests against analytic limiting cases so you catch subtle numerical bugs automatically.
What you'll haveFar more modeling and analysis shipped per week — the computational output that commands top pay.
2
Build ML surrogates and physics-informed models
Why this pays: Surrogate models and physics-informed neural networks replace expensive simulations and open roles in industry R&D and quant finance — the highest-paying destinations for physicists, well into the $200k+ range.
PyTorchJAXscikit-learn
1
Learn to build surrogates in PyTorch or JAX: train a network to emulate a costly simulation, then use it for fast optimization and inference.
2
Scaffold a physics-informed neural network.
Copy-paste this prompt
Explain and give me starter JAX code for a physics-informed neural network that solves [the 1D heat equation / Burgers' equation] with boundary conditions [state them]. Include the PDE residual loss, the training loop, and how to validate against the analytic or finite-difference solution.
Validate the ML solution against a trusted numerical solver; a low training loss is not proof of a correct solution.
3
Package one surrogate or PINN project into a portfolio piece — it's the concrete evidence that gets you industry and quant interviews.
What you'll haveA scientific-ML skill set and a portfolio project — the crossover that opens $200k+ industry and quant roles.
3
Crush the literature and find the gap
Why this pays: Publishing more, and in the right gaps, builds the record that wins grants, promotions, and offers. AI turns weeks of literature review into days, so your time goes to original work.
ElicitConsensusNotebookLM
1
Use Elicit to extract methods and results across dozens of papers into a table, and Consensus to check what the literature actually concludes on a question.
2
Map an unfamiliar subfield fast.
Copy-paste this prompt
I'm entering [topological photonics]. Give me the 12 most important papers to read in order, the key open problems, the main experimental groups, and 3 gaps where a new contribution would matter. Note which claims are contested.
Verify every recommended paper actually exists and read the primary sources — AI invents citations.
3
Load a stack of PDFs into NotebookLM and query it ('which of these use method X, and what did they find?') to synthesize a review section.
What you'll haveA faster path to well-targeted publications — the record behind grants, promotion, and offers.
4
Win grants and write papers faster
Why this pays: In academia and labs, grant funding is your salary and your group. AI helps you draft, sharpen, and revise proposals and papers faster and clearer — more funding, more output, more comp.
ClaudeChatGPTOverleaf
1
Pressure-test a proposal before a panel does.
Copy-paste this prompt
Here is my specific-aims / project-summary draft for a [DOE / NSF] proposal on [topic]. Critique it as a skeptical review panel: is the significance clear, are the aims independent, what is the weakest claim, and where do I need preliminary data? Then suggest a tighter one-paragraph summary.
AI critiques and polishes; the science and every number must be yours and verified. Follow the funder's AI-use policy.
2
Draft, tighten, and format in Overleaf with AI help for LaTeX, figures, and cover letters, then adapt one paper into a talk and a plain-language summary.
3
Use AI to draft point-by-point responses to reviewer comments, then verify every technical reply yourself.
What you'll haveMore funded proposals and published papers per year — the direct engine of academic and lab comp.
5
Pivot to high-paying industry, quant, or national-lab roles
Why this pays: The salary gap between a postdoc and an industry or quant physicist is enormous. AI helps you translate physics skills into the language those employers hire for, and prep the interviews — the jump into the $200k+ tier.
ChatGPTClaudeLeetCode
1
Translate your CV for the target industry.
Copy-paste this prompt
I'm a physicist with skills in [computational modeling, Bayesian inference, signal processing]. Rewrite my experience for a [quantitative researcher / semiconductor R&D / ML engineer] role: map each physics skill to what the employer wants, and list the 5 technical topics I should be ready to be grilled on.
Keep every claim truthful; use AI to reframe real experience, not inflate it.
2
Drill quantitative and coding interviews with LeetCode and AI-generated probability and brainteaser problems common in quant and tech screens.
3
Have AI mock-interview you on physics fundamentals and your own projects, and critique your answers.
What you'll haveThe reframing and prep to land an industry, quant, or senior-lab offer — the move into the top of the range.
6
Automate experimental data pipelines
Why this pays: Faster, more reliable data reduction means more results and more first-author output from the same experiment — the productivity that earns authorship, promotion, and reputation.
PythonGitHub CopilotPandas
1
Build reproducible analysis pipelines (Python + Pandas) with Copilot: automated calibration, error propagation, and figure generation from raw data.
2
Set up robust uncertainty analysis.
Copy-paste this prompt
Write Python to propagate uncertainties through this calculation [describe the measurement and formula], including correlated errors, and produce a publication-quality plot with error bars and a fit reporting reduced chi-squared. Explain the statistics you used.
Confirm the statistical treatment fits your data; AI can silently apply the wrong error model.
3
Version pipelines in Git so results are reproducible — the standard reviewers and labs expect.
What you'll haveMore reliable results per experiment and cleaner reproducibility — the output record behind advancement.
Your 12-month sequence to the top of the range
How the plays above stack into a path from median pay toward the $296,740 tier.
Month 1
Adopt an AI coding assistant and speed up an existing analysis; start mining your subfield's literature with Elicit and NotebookLM.
Months 2-3
Build one ML surrogate or PINN project and a reproducible data pipeline you can show.
Months 3-6
Use AI to accelerate a paper and a grant proposal, verifying all math, data, and citations by hand.
Months 6-12
If targeting industry or quant, translate your CV and drill interviews; otherwise deepen the computational record that earns senior and lab comp.
Gear for this job
As an Amazon Associate, PayCrunch earns from qualifying purchases. Links to books and tools are for the job on this page; we only recommend what we’d use in the work.
Same live O’Reilly 3rd already on data-scientist / python-developer / market-research-analyst / biochemist / botanist. This page’s Automate experimental data pipelines play names Python + Pandas for calibration, error propagation, and figure generation. Not CompTIA Data+ and not leftover Ross Exam P (that is actuary).
Next steps for a Physicist
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.
Physicist work is specific enough that a stamped 'check out these courses' block would be noise. BLS files this work as Physicists (SOC 19-2012). O*NET Job Zone 5 is typical: graduate or professional school, so the honest next credential is a graduate-level or professional certificate — not a random catalog dump.
The occupation's listed knowledge areas include Physics and Engineering and Technology; the links search those subjects, not a generic 'career courses' list.
Physicists in this dataset list Amazon Web Services AWS software among the tools in use, so a program that names that stack is a better fit than a survey course.
FlexJobs screens remote, hybrid, freelance, and flexible listings so you are not wading through unverified ads. This is a job-board search for Physicist work, not a claim that they list a counted SOC 19-2012 inventory.
A a Physicist resume you can submit beats a blank page. Resume Now is a resume builder — we are not claiming an occupation-specific template library for SOC 19-2012.
A Physicist resume that names the actual tasks on this page beats a blank template when you apply.
What Physicists earn by state
These are the Bureau of Labor Statistics’ own figures for Physicists, state by state — not a cost-of-living adjustment applied to the national number. Only states employing at least 500 people in the occupation are shown, because a state median drawn from a handful of workers is noise rather than a signal.
California
$207,490
highest of them · +20% vs the national median
Michigan
$94,520
lowest of the 11 states that qualify · -45% vs the national median
The same job pays $112,970 more a year at the median in California than in Michigan — 120% higher. That gap is what the Bureau measured, before any question of what it costs to live in either place. California also carries the top of this job’s range, $296,740 — the figure quoted at the head of this page.
Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025, SOC 19-2012. 11 states clear the 500-employee reporting floor for this occupation; those below it are left out rather than shown with a wide error band.
Free data. Use any of it.
PayCrunch publishes verified, BLS-sourced salary + AI-playbook data on 1,000+ professions — free, no signup.
No — it's transforming how physics is done, not who does it. AI accelerates coding, literature review, and modeling, but it hallucinates math and can't design an experiment, judge a result's physical meaning, or take responsibility for a claim. The physicists who master AI tools will dominate output and pay; those who ignore them fall behind on productivity.
Can I trust AI-generated derivations and code?
Only after you verify them. LLMs routinely produce confident, wrong math and code that runs but computes the wrong thing. Check derivations by hand, test code against analytic limits, and reproduce every number before it enters a paper. Treat AI as a fast, fallible collaborator.
What's the highest-leverage AI skill for physics pay?
Scientific machine learning — building surrogate models and physics-informed networks in PyTorch or JAX. It's the crossover skill that opens the highest-paying destinations (quant finance, semiconductors, quantum computing, industry R&D), which is where the $297k tier lives.
Is it safe to use ChatGPT at a national lab?
Not for anything export-controlled, proprietary, ITAR, or classified — that's a serious violation. Use approved, on-premises, or vetted tools for sensitive work, and reserve consumer AI for open, unclassified tasks like public-literature review or general coding. Follow your institution's policy.
How do I move from a postdoc salary to $200k+?
Usually by moving into industry, quant finance, or a senior lab or leadership role, all of which reward computational and ML skill. Use AI to build a scientific-ML portfolio, translate your physics experience into the employer's language, and prepare for their technical interviews.
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.