$199,280estimated top of the range · middle $98,000 / yr
AI is transforming this role
Immunologists in the United States earn a median of $98,000 a year. Pay starts near $60,000. The top of the range is estimated at $199,280. 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
$60,000
Top-end estimate
$199,280
Education
Doctoral degree in Immunology
Wages — PayCrunch estimate. The Bureau of Labor Statistics does not publish a separate wage series for Immunologist; 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 ImmunologistReviewed September 2026
We track new AI-tool launches every week and refresh this list — here’s what’s gaining traction for Immunologist work right now.
Julius AINEWFree / $20 mo
AI data analyst that runs statistics and charts from plain-language prompts.
How an Immunologist 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 Immunologist 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 Immunologist 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 Immunologist uses it: get evidence-backed answers with the studies behind them
SciSpaceFree / paid
AI that explains papers and helps with literature review.
How an Immunologist 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 Immunologist 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 Immunologist 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 Immunologist 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 Immunologist uses it: draft and reply inside Google Workspace and research without leaving the page
Physician, scientist, or a path that touches both
Immunology is the study of how bodies defend themselves, and an immunologist is someone who makes that study a career. Two doors open onto the field. One is medicine: you become a physician and care for patients whose immune systems are part of the problem. The other is science: you become a researcher in a university, a public-health lab, or a company, and your product is knowledge, a paper, or a finding a team can build on. Some people do a version of both, especially in academic medicine, where a clinic and a research program share a week. The title on a job posting should tell you which door you are walking through. If it does not, ask before you plan your life around it.
The physician's day is patients, records, and colleagues who need a clear opinion. You see people referred because something about their immune response needs a specialist. You take a history, you decide which evaluations belong in the workup, and you explain the result in language a person can use. You write notes other doctors can follow. You talk with primary care, with hospital teams, and sometimes with schools or employers who need to understand a limitation without receiving a lecture. The manner matters as much as the knowledge. People arrive worried. They leave with a plan they can repeat, or with an honest statement that more time is required.
The scientist's day is a question the lab is built to pursue, data, and writing. You design studies with a team, you interpret what the immune response did under the conditions you chose, and you say what the result does and does not support. You read other people's papers and you argue, politely, in meetings. A company scientist may aim at a product. A university scientist may aim at a mechanism and at the next grant. A public-health scientist may aim at a population pattern. None of that is a clinic, and none of it requires you to pretend it is. The shared thread is the immune system. The daily proof of work is different, and so is the training.
Medical school, residency, fellowship, and a state licence
If you will see patients, you train as a physician. That means a bachelor's degree, then medical school leading to an MD or a DO, then a residency, then a fellowship. The residency is usually internal medicine or pediatrics, because that is where you learn to be a doctor for adults or for children. The fellowship is in allergy and immunology, which is where this specialty is actually taught. People sometimes hope a short course can stand in for that sequence. It cannot carry a medical practice. The fellowship is the point at which the specialty becomes yours, after the residency has already made you a physician.
You also need a state medical licence in each state where you practice, including by video when that care counts as practice there. A state medical board grants the licence. It is the legal permission to practice medicine. Hospitals, insurers, and group practices add their own credentialing on top, and that paperwork moves slowly, so start it before you expect a full clinic. The licence and the specialty training do different jobs. Residency and fellowship make you prepared. The licence makes the practice lawful. Keep the board's requirements for the state you want, and do not borrow another state's checklist from memory.
Many physician immunologists also seek board certification in allergy and immunology through the American Board of Allergy and Immunology. That board grants the certificate. It tells hospitals and colleagues that you completed the recognized training and met the board's requirement for certification. It is separate from the state licence. Read the board in its own words at abai.org. Some employers hire physicians who are still on the way to the certificate, with a date written into the offer. What they rarely accept is a vague promise with no timeline. The licence lets you practice. The certificate is how the specialty recognizes its physicians.
A research career beside the clinic
If you will not practice medicine, do not collect a medical licence you will never use. The scientist path is a bachelor's degree in biology or a related field, then usually a PhD in immunology or a close discipline. Postdoctoral work is common: you join a group, learn how that group publishes, and find out whether you want to lead a program or stay close to the bench as a senior scientist. Industry hires PhD immunologists into discovery teams. Universities hire them into faculty or research-track roles. Public-health and government labs hire them to study immune patterns that matter to a population. A master's degree can support a research-associate career. It is a different destination from directing a lab, and it should be chosen on purpose.
Academic physicians sometimes hold both kinds of training: the MD, the residency, the fellowship, and a serious research period. That path is long, and the week is split between clinic, grants, and trainees. Choose it because you want both duties, not because it sounds complete. A pure scientist who misses patients should not drift into clinic without a licence and a residency. A physician who misses the bench can collaborate with a lab without pretending to be the principal investigator. Name the product of your job in one sentence. If the sentence is "I see patients," you need the physician stack. If the sentence is "I study immune responses and report what we find," the scientist stack is the honest one.
Two licences to keep straight
A state medical licence is what lets a physician practice. Board certification from the American Board of Allergy and Immunology is the specialty credential. A research scientist who does not practice medicine does not need either one.
Clinics, labs, and public-health offices
Physician immunologists work in specialty clinics, academic medical centers, and sometimes in hospital consult services. The hiring group wants a fellowship, a licence that covers the state of the clinic, and a manner with patients that nurses will confirm. References should include the fellowship director and someone who watched you explain a hard result. A community group and a faculty job ask for different mixes of clinic time and teaching. Ask what portion of the week is patients, who covers you when you are away, and how referrals actually arrive. A beautiful title with an empty schedule is a slow start. A full panel with good staff can be the better first job.
Labs hire on the science and on the way you work with other people. Bring a talk you can give without slides full of clutter, a paper or a chapter you wrote, and a clear account of what you contributed versus what the group contributed. Principal investigators notice candidates who claim an entire project they only touched. Industry hiring adds a conversation about timelines and about working inside a team that includes people who are not immunologists. Government labs add a formal application and a slower clock. In every setting, ask who reviews your work and what a good first year produces: a clinic panel, a published study, a method the group adopts, or a product milestone described in ordinary words.
The middle of the career is where the two paths stay different. Physicians become partners, section chiefs, or the senior clinician younger doctors call. Scientists become group leaders, directors of a research unit, or the specialist a company trusts with a hard problem. Some move from academia into industry, or from a clinic into a role that is mostly teaching and administration. Pay follows scope more than it follows the word immunologist alone. A new fellowship graduate and a physician who runs a regional clinic should not expect the same offer. A new postdoc and a scientist who leads a funded program should not either. Title the offer by the work, then look at the estimates below as a national sketch, not as a personal quote.
Why these dollars are estimates
The Bureau of Labor Statistics does not publish a separate wage series for this exact title. The figures here are PayCrunch estimates, built for an immunologist rather than borrowed from a neighboring occupation and relabeled. Entry on the estimate is $60,000. The median estimate is $98,000. The gap from entry to the median is $38,000. That step is wide enough to cover very different early jobs: a research salary near the start of a science career, and a physician still finishing training, sit in a different world from a practicing specialist. Use $60,000 as the entry estimate. Use $98,000 when the role is a full professional job with independent work.
The estimated top is $199,280. The gap from the median to that top is $101,280. Call it the high end of the estimate. It is not a typical wage, and it is not attached to any state. These estimates include no state medians at all. If a recruiter pins $98,000 or $199,280 to a place, they are adding a fact the estimate does not contain. Leave geography out of the number. Talk about whether you are a physician with a licence and a fellowship, or a scientist with a PhD and a record of studies, and about whether you lead other people. Those facts move an offer. A state name glued to an estimate does not.
Estimates, with no state figure
The Bureau of Labor Statistics does not publish a separate wage series for an immunologist. $60,000, $98,000, and $199,280 are PayCrunch estimates. They are not state medians.
Keep the three numbers together when you compare postings: $60,000, $98,000, and $199,280. The only gaps to cite are $38,000 and $101,280. A posting that quotes a much larger physician table, or a much smaller technician table, is talking about a different occupation. You can listen, and then you can return to this estimate and say why. Training stipends during residency or a postdoc often sit near or below the entry estimate and should be labeled as training pay, not as the wage for a finished immunologist. Once the fellowship or the independent science role begins, the median is the more serious national comparison.
Talking salary from an estimate
Say the source before you say the number. These are PayCrunch estimates because the Bureau of Labor Statistics does not publish a separate wage series for this exact title. Then place your offer beside $60,000 or $98,000 based on scope. A physician who has finished residency and fellowship, holds a state licence, and is building a panel has a reason to treat $98,000 as a floor to discuss, not as a trophy, and to talk about how far the role sits above that median. A scientist in a first permanent role can use the same median with a different story: independent projects, not a training stipend. The $38,000 gap is the size of the step from the entry estimate to that midpoint. It is a national sketch. Your offer still has to name the duties.
The $101,280 between $98,000 and $199,280 is the wide upper part of the estimate. Leadership, a scarce clinical skill paired with a full panel, or a senior science role with a program to run can belong in that stretch. Quoting $199,280 for a first faculty appointment or a first industry job skips the scope. Bring the licence or the doctorate, the setting, and the people you will supervise or teach. Academic offers often mix salary with protected time and startup support. Industry offers may mix salary with a bonus. Ask for those pieces in writing. Compare the salary itself with the estimate so a large bonus story does not hide a thin base.
Write four lines before you accept. Physician or scientist. The base they offered. Whether that base sits nearer $60,000, nearer $98,000, or somewhere toward $199,280 with a reason. And the credential that matches the path: MD or DO, residency, fellowship, and a state licence for the physician, or the research degree and the record of studies for the scientist. Board certification can support a physician's case. It does not replace the licence, and it does not apply to a scientist who never practices. Because no state median exists in this estimate, end the money talk without one. The work you can name, and these three figures, are the whole comparison.
The top of Immunologist pay — and how to get there with AI
$199,280top-end estimate for Immunologist
PayCrunch estimate - derived from the closest occupation BLS tracks (Healthcare Diagnosing or Treating Practitioners, All Other, 29-1299). This figure is PayCrunch’s estimate, not a Bureau of Labor Statistics published wage for this exact title.
And the role it leads to — Neurologists — reaches $449,840 in Texas.
$60,000entry$98,000middle$199,280top end
The immunologists at the upper end of this range decide how a test is performed and interpreted across their institution, instead of ordering one and waiting for somebody else's number to arrive.
Performing diagnostic tests and measurements, preparing diagnostic and treatment reports for other practitioners, and taking part in clinical research projects all reward a precision that nobody audits. Repeatability between operators, drift between reagent lots, how a borderline finding is worded: these decide whether a patient carries a label for the rest of their life, and in most services no physician owns any of them. Pulling two years of results out of the record and asking whether they hang together is now a fortnight rather than a study, because a model will restructure a messy export and Microsoft Excel handles the rest. Whoever does it becomes the person the laboratory and the referring physicians both ring.
Your playbook, by where you are now
Just startingBe exact before you are busy
Perform the tests with your own hands often enough to know how they fail, rather than reading only the printed result.
Write your own reporting template so every report you send another practitioner states the method, its limits and your confidence.
Keep a structured record of your own cases in Microsoft Excel from the first month, because retrospective work is only possible if the columns already exist.
Read primary literature through online medical databases weekly and summarise one paper for colleagues each month.
Give patients and families written instructions covering the diagnosis and the treatment plan, and keep every version you issue.
What proves it: A reporting template other clinicians in your service have adopted.
Realistic span: fellowship and the two years after it
A few years inAudit what your service actually produces
Take two years of your service's results and examine them for operator variation, seasonal drift and inconsistent borderline reporting.
Ask Claude to reshape the raw export into a tidy table, then verify a sample of rows against the source record before trusting a single figure.
Publish or present the audit, since presenting scientific papers is on this occupation's task list and almost nobody does it from routine data.
Join the laboratory's method committee so assay changes reach you before they reach your patients.
Take a place on a clinical research project as the person accountable for data quality, a role that never has enough volunteers.
What proves it: A presented or published audit of your own service's diagnostic accuracy.
Realistic span: years three through seven
ExperiencedSet the protocol others work to
Write the institution's protocol for the tests and challenges you supervise, with stopping rules and reporting standards fixed in advance.
Provide training in the method itself to residents and other health professionals, not only in how to read the output.
Take referral work in the narrow area where your accuracy record gives you standing, because referrals follow a named physician.
Lead a clinical research project rather than contributing to one, and hold the data yourself.
Texas pays this occupation more than other states, and subspecialty referral practice is where the range widens further still.
What proves it: An institutional testing protocol carrying your name, and a research project you lead.
Realistic span: eight years and up
The next 90 days
Spend a quarter auditing one thing you put your signature to. Export every result of a single test your service produced across the past two years, together with who performed it, when, and exactly what was reported. Then look for what nobody looks for: differences between operators, results clustering suspiciously near a cutoff, borderline findings described inconsistently from one letter to the next. Write four pages and take them to the laboratory director. Even if the conclusion is that everything is sound, you now hold the only description of your service's accuracy that exists anywhere, and whoever holds that gets consulted on every protocol change afterwards.
Wage figures: PayCrunch estimate. 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).
Start with structure prediction. Open the AlphaFold 3 server (free for non-commercial use) and predict a structure you already know - an antibody-antigen complex or a receptor - to learn what it gets right and where it's uncertain (read the confidence metrics). This is the fastest way to build intuition for AI in immunology.
For sequences and analysis, explore ESM3 (a protein language model) and use Claude or ChatGPT to write the Python (Biopython, scanpy) for your repertoire and single-cell work, with Elicit for the literature. Keep any confidential sequences or patient data out of consumer tools.
The one rule, forever: AI predictions in immunology are hypotheses to be tested, never conclusions. Structure predictions, designed binders and epitope forecasts are wrong often enough that acting on them without wet-lab validation is dangerous - confirm binding, function and immunogenicity experimentally, follow biosafety, dual-use and IRB/IACUC rules, and never put patient data, sequences under confidentiality agreement, or proprietary IP into consumer AI tools.
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
Model antibody-antigen structure and interactions
Why this pays: Knowing where and how an antibody binds turns blind screening into rational design. Using AlphaFold 3 and protein language models to predict complexes and hotspots lets you prioritize the constructs worth making - the structure-guided speed that principal-scientist roles in antibody biotech are paid top-of-range for.
AlphaFold 3EvolutionaryScale ESM3ChimeraX
1
Predict antibody-antigen complexes in AlphaFold 3, inspect the interface and confidence (pAE/pLDDT) in ChimeraX, and use ESM3 to reason over sequence variants before committing to synthesis.
2
Plan a structure-guided characterization of an antibody.
Copy-paste this prompt
I'm characterizing an antibody against [target antigen]. Given the CDR sequences [paste] and the antigen [paste], outline how to use AlphaFold 3 to predict the complex, which confidence metrics tell me whether the predicted epitope is trustworthy, the interface residues to prioritize for mutagenesis, and the wet-lab experiments that would confirm the predicted binding mode.
Predicted complexes - especially antibody-antigen interfaces - are frequently wrong; treat the epitope as a hypothesis and confirm with binding assays or structural data before designing around it.
What you'll haveStructure-guided prioritization of antibody constructs - the rational-design speed that defines a top-of-range antibody scientist.
2
Design and optimize binders with generative AI
Why this pays: A de novo binder or an affinity-matured antibody is the deliverable biotech pays the most for. Generative design tools propose candidates in silico that would take months to find by screening - the immunologist who can drive them and validate the hits accelerates a program straight toward role at the top of the ranges and milestone equity.
RFdiffusion / RFantibodyProteinMPNNAlphaFold 3
1
Generate binder backbones with RFdiffusion/RFantibody, design sequences with ProteinMPNN, and filter in silico with AlphaFold 3 before ordering only the top candidates for the bench.
2
Design a de novo binder campaign end-to-end.
Copy-paste this prompt
Design a plan to generate a de novo mini-binder against [target/epitope]: how to set up RFdiffusion for that hotspot, use ProteinMPNN for sequence design, filter with AlphaFold 3 metrics (iPTM, pAE) to shortlist candidates, and the wet-lab funnel (expression, SPR/BLI affinity, functional assay) to validate them. Estimate how many designs to order for a realistic hit rate.
In-silico hit rates are low and the metrics are imperfect proxies - order a sensible number, validate binding and function experimentally, and observe dual-use and biosafety review for any designed protein.
What you'll haveIn-silico-designed, wet-lab-validated binders - the high-value deliverable that accelerates programs and pays top-of-range.
3
Predict epitopes and immunogenicity
Why this pays: Picking the right epitope makes or breaks a vaccine, and the immunogenicity profile makes or breaks a biologic. AI epitope and MHC-binding prediction narrows a huge search space to testable candidates and de-risks therapeutics - the design edge that vaccine and immuno-oncology programs pay at the top of the range for.
NetMHCpanIEDB Analysis ResourceAlphaFold 3
1
Predict T-cell and B-cell epitopes and MHC binding with NetMHCpan and the IEDB tools, map them onto structures from AlphaFold 3, and rank candidates for immunogenicity screening.
2
Build an epitope and immunogenicity strategy from sequence.
Copy-paste this prompt
For [a vaccine antigen / a therapeutic protein] with sequence [paste], design an epitope strategy: use NetMHCpan and IEDB to predict [Class I and II] epitopes across common HLA alleles, identify conserved and surface-exposed regions using the AlphaFold structure, flag potential immunogenicity liabilities for a biologic, and propose the assays to validate the top epitopes.
Epitope predictors have high false-positive rates and HLA coverage gaps - treat rankings as a shortlist for ELISpot, tetramer or MHC-binding assays, not as established epitopes.
What you'll haveA ranked, testable epitope shortlist - the design edge that de-risks vaccines and biologics and commands pay at the top of the range.
4
Analyze single-cell and immune-repertoire data with ML
Why this pays: Modern immunology is drowning in single-cell and repertoire data, and the insight is worth grants, papers and IP. The immunologist who can run scRNA-seq, TCR/BCR repertoire and multi-omic ML pipelines - with AI writing much of the code - produces the findings that drive promotion to senior scientist and pay at the top of the range.
Scanpy / SeuratImmcantation (TCR/BCR)scGPT + Claude
1
Build reproducible pipelines in Scanpy/Seurat for scRNA-seq, Immcantation for TCR/BCR repertoire, and explore foundation models like scGPT - using Claude to write and debug the code.
2
Generate an annotated single-cell plus repertoire pipeline.
Copy-paste this prompt
I have [10x scRNA-seq with paired TCR] data from [tumor vs blood, N donors]. Write a Scanpy pipeline: QC and doublet removal, integration across donors, clustering and annotation of [T-cell subsets], differential expression between conditions, and linkage to TCR clonality. Explain each parameter choice and where batch effects could mislead me.
AI-written single-cell code runs but hides pitfalls - verify QC thresholds, integration and cluster annotations against known biology; batch effects and doublets create fake findings.
What you'll havePublication-grade single-cell and repertoire insights produced fast - the science that drives grants, IP and promotion.
5
Accelerate the literature, grants and lab record
Why this pays: Immunology moves weekly and funding is fierce. AI literature synthesis and grant drafting let you stay current and submit more competitive proposals, while structured records keep your data grant- and IP-ready - the pipeline of funding and publications that builds a top-of-range research career.
Elicit / ConsensusBenchlingClaude
1
Synthesize the fast-moving literature with Elicit/Consensus, keep protocols and data in Benchling, and draft NIH or biotech proposals structurally with Claude from your own results.
2
Draft a specific-aims page grounded in your data.
Copy-paste this prompt
Help me draft the specific-aims page for an [NIH R01] on [the mechanism of T-cell exhaustion in X]: a central hypothesis, three aims with rationale, approach and pitfalls-and-alternatives, and a significance paragraph tying to unmet need. Use only claims my results support [paste notes] and mark every place that needs a citation.
AI fabricates references and can over-claim - insert only citations you've verified, keep every claim within your data, and follow your funder's AI-disclosure policy.
What you'll haveMore competitive grants and faster literature mastery - the funding-and-publication engine behind a top-of-range immunology career.
Your 12-month sequence to the top of the range
How the plays above stack into a path from median pay toward the $150,000 tier.
Month 1
Predict a known antibody-antigen complex in AlphaFold 3 and learn to read its confidence metrics.
Months 2-3
Run an epitope-prediction and structure-mapping analysis on a real antigen.
Months 3-6
Trial a generative binder-design pipeline (RFdiffusion to ProteinMPNN to AlphaFold filter) and plan the wet-lab validation.
Months 6-9
Build a reproducible single-cell and repertoire ML pipeline for your own data.
Months 9-12
Use AI to draft your next grant and synthesize the literature - the funding engine.
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 Jossey-Bass 3rd already on high-school-teacher / middle-school-teacher / math-teacher / test-prep-instructor / substitute-teacher / science-teacher / music-teacher / drama-teacher / adult-education-teacher / corporate-trainer / instructional-designer / stem-teacher / pe-teacher / speech-teacher / curriculum-developer / education-consultant / college-professor / assistant-principal / financial-literacy-educator / school-principal / vice-principal / homeschool-consultant / school-administrator / edtech-specialist / education-administrator / distance-learning-coordinator / capitol-police-officer / tsa-agent / piano-tuner / birth-doula / dive-master / translator / voice-over-director / wordpress-developer / balloon-artist / circus-performer / nutritionist / academic-advisor / dermatologist / train-conductor / calligrapher / choreographer / motivational-speaker / marble-polisher / compensation-analyst / fleet-manager / music-producer / iot-engineer / it-director / media-buyer / hospital-administrator / ship-broker / dean / clinical-pharmacist / dental-surgeon / casino-dealer / coroner / digital-transformation-consultant / sheriff / financial-crime-investigator / emergency-medical-dispatcher / railroad-engineer / correctional-officer / healthcare-consultant / compliance-officer / organ-transplant-coordinator / dispatcher / county-clerk / parole-officer / customs-officer / census-taker / patent-attorney / quantum-computing-researcher / regulatory-affairs-specialist / game-designer / dental-therapist / recruiter / web-content-manager / magistrate (ASIN 1119712610). This leftover page is a PayCrunch estimate (BLS tracks Healthcare Diagnosing or Treating Practitioners, All Other, 29-1299); title is Own the Testing and Its Accuracy; H1 is The immunologist who decides how results are read; just-starting track is Be exact before you are busy; few-years track is Audit what your service actually produces; experienced track is Set the protocol others work to; the playbook centers writing the institution's protocol for the tests and challenges you supervise, then providing training in the method itself to residents and other health professionals, not only in how to read the output, with proof being an institutional testing protocol carrying your name and a research project you lead; start-here is Start with structure prediction on the AlphaFold 3 server; one-rule is AI predictions in immunology are hypotheses to be tested, never conclusions — confirm binding, function and immunogenicity experimentally. This instructional-technique guide directly supports that protocol/method instructional work. Classroom technique for leftover instructional work — not leftover Wong as the lead (that is auto-appraiser / delivery-driver / mover / ombudsman / producer / toxicology-technician) and not leftover Praxis as a dump. Confirm 1119712610. Live page HTTP 200, no PC_GEAR / amazon.com/dp / tag=paycrunch-20 at 2026-09-18 7:40:04 AM PT. Source page: high-school-teacher.
What Immunologists 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 $60,000, the median is $98,000, and the top of the range is $199,280. Those national figures are a PayCrunch estimate, not a Bureau of Labor Statistics published wage for this exact title.
No. AI predicts and designs, but the immune system's complexity means predictions must be tested at the bench, and no model owns the biology or the clinical judgment. Immunologists who pair AI design with wet-lab validation move programs years faster; those who don't fall behind.
Can I trust AlphaFold 3 for antibody-antigen complexes?
Only as a hypothesis. Antibody-antigen interfaces are among AlphaFold's weakest predictions - use the confidence metrics (pAE, iPTM) to judge reliability and confirm the epitope experimentally before designing around it.
Are AI-designed antibodies and binders real?
Some are. In-silico hit rates are modest and the scoring metrics are proxies, not proof. Use generative design to enrich your candidate pool, then validate binding and function in the lab - the design is a starting point, not a result.
Is it safe to put sequences or patient data in AI tools?
Not proprietary sequences under a confidentiality agreement or any patient data in consumer tools. Use approved or enterprise environments, and mind dual-use and biosafety review for any designed protein.
How does this raise my pay?
By compressing discovery cycles in antibody, vaccine and immuno-oncology programs and producing high-value data and IP - the work that earns principal-scientist roles and milestone-driven, top-of-range compensation in biotech and pharma.
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.