PayCrunch Research · The exact AI playbook for your profession, sourced to the U.S. Bureau of Labor Statistics

PayCrunch AI Playbook · Science

The genetic engineer whose data summaries build themselves

$194,760top of the range in Massachusetts · middle $109,370 / yr
AI is transforming this role

Genetic Engineers in the United States earn a median of $109,370 a year. Pay starts near $71,850. Pay reaches $194,760 at the top of the range in Massachusetts, 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 (Bioengineers and Biomedical Engineers, SOC 17-2031). Last checked 9 September 2026.

Entry level
$71,850
Top of the range · Massachusetts
$194,760
Education
Doctoral degree in Genetics or Bioengineering
Lower disruption Higher exposure AI is transforming this role
Entry · $71,850 Top of range · $194,760 (Massachusetts) Middle $109,370

Wages — U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025 (Bioengineers and Biomedical Engineers). 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 Genetic EngineerReviewed September 2026

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

Julius AINEWFree / $20 mo

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

How a Genetic Engineer 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 Genetic Engineer 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 Genetic Engineer 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 Genetic Engineer uses it: get evidence-backed answers with the studies behind them

SciSpaceFree / paid

AI that explains papers and helps with literature review.

How a Genetic Engineer 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 Genetic Engineer 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 Genetic Engineer 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 Genetic Engineer 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 Genetic Engineer uses it: draft and reply inside Google Workspace and research without leaving the page

The seat they are actually hiring

A genetic engineer, in the way companies use the title, holds a seat at a therapeutics firm, an agricultural or industrial biotechnology company, a hospital-linked research group, or a university lab. The posting is for a person who can own a project. You are not being asked, in a career conversation, to recite a method. You are being asked where you will sit, who you will report to, and what kind of week the team already runs. I hire for that seat. I want a degree I can verify and a plain account of a project you were responsible for.

Company seats and lab seats feel different by Friday even when the diploma matches. At a company you share a floor with quality, manufacturing, and a manager who has to explain the project to people who will never see the bench. At a university lab you share a floor with a principal investigator, graduate students, and a grant calendar. Both seats still need someone who can say, in ordinary language, what the project is trying to accomplish, who is doing which part, and whether the timeline is still honest. That is the job I can describe. The scientific technique stays inside the employer's own training, with the employer's own oversight.

If a recruiter's pitch turns into a tour of how organisms are changed, step back to the seat. Ask which group you would join, whether the role is individual contributor or people lead, and what a finished month of work looks like in documents and meetings. Candidates who can answer those points are the ones I can place. Candidates who answer with a procedure are answering a different conversation than the one that leads to an offer.

A week told as a job, not as a method

Monday usually starts with a project meeting. You report status in sentences a manager can repeat: what moved, what stalled, and what decision you need. You listen to the other projects only long enough to know where your work depends on theirs. The useful skill in that room is brevity. A rambling account of activity reads as a project that does not yet have an owner.

Midweek you read the written summaries the team already produced. Your job is to understand the outcome well enough to update the timeline and to write a short note for your manager. You might sit with a quality colleague or a regulatory colleague and talk about whether the file for a milestone is complete: are the records there, are the decisions dated, is the next review on the calendar. You might sit with a manufacturing planner or a lab manager and talk about people and schedule. None of that asks you to walk anyone through bench work. It requires you to know what the project promised and whether the promise is still intact.

Later in the week you present. A small audience inside the company, or a lab meeting, hears what changed and what you recommend for the next block of work. Recommendations at this altitude sound like scope, staffing, and a go or wait decision. They do not sound like instructions for making or altering a living thing. If you feel the presentation sliding that direction, stop and describe the job again: who decides, what document captures the decision, and when the group will look at it next.

The rest of the week is coordination. You answer the teammate who needs a priority. You keep a running note so a person out sick can pick up the status. You join a cross-functional conversation with clinicians, product staff, or a principal investigator, depending on the seat, and you translate schedule risk into plain speech. New hires often think the week will be a single heroic experiment. The week I hire for is a string of decisions, records, and conversations that keep a genetics-related project from becoming a pile of activity with no owner.

The degree that opens the door

The common door is a bachelor's degree in bioengineering or biomedical engineering. Related degrees in biological engineering, chemical engineering with a life-science focus, or a rigorous life science paired with real engineering coursework also show up in hiring piles. The degree is evidence that you trained in that frame. It proves you can be taught the employer's projects. It does not, by itself, prove you can run a team or talk to a regulator. Those show up later, on the job.

Some company research seats and many university seats prefer a master's or a doctorate. Read the posting for that preference instead of assuming every genetic-engineer title means the same school path. A doctorate signals a long research training and a habit of owning a question through to a written result. A bachelor's degree with strong internships can still win a company seat that is about project execution beside more senior scientists. I would rather see an honest match between the degree and the seat than a candidate who inflates a school project into a fake leadership story.

Prepare by finishing the degree, by taking internships or lab roles you can describe in plain language, and by learning to write. A one-page project summary that names the problem, your responsibility, and the outcome will do more for you than a slide full of jargon. Leave methods out of that summary when you are job hunting. Say what the team delivered and what you were accountable for. Supervisors can confirm that story. They cannot confirm a procedure you improvised in an interview.

This seat is hired on the degree and the project record. A professional engineer stamp belongs to other engineering work that puts a licence on public drawings. You do not need to pretend you are on that track to be credible here. If you do hold some other credential, mention it only when the posting asks. The conversation I want is still the degree, the seat, and the week.

What a hiring manager can check

I can check the school, the dates, and whether a supervisor remembers you as the person who kept the project intelligible. I can read a writing sample with confidential detail removed. I can ask you to walk me through a project as a story with a beginning, a responsibility, and an outcome. I cannot use the interview as a place to collect a method, and I will stop a candidate who offers one. That boundary protects the company and it protects you from inventing detail you should not be sharing from a prior employer.

Tell me which seat you want. A manufacturing-adjacent company role, a discovery group, and a university lab are three different weeks. If you want hands-on bench time, say so, and then still describe the job as time, supervision, and records rather than as steps. If you want the coordinator role that sits between scientists and a director, say that too. Mismatched appetite is the most common reason an offer fails after a good first conversation. I would rather place you in the group that matches your week than praise you and then watch you leave in six months.

References should be people who saw your writing and your reliability. A professor who can say you finished what you started is useful. A supervisor who can say other people could understand your status notes is more useful. Bring any constraint early: a city, a visa, a start date after a thesis defense. Small teams hire one person at a time. A late surprise costs them a quarter, and they will remember the candidate who was clear.

New hire, project owner, then a lead

You start by learning how that employer tracks work. Templates, meeting cadence, and the names of the people who can say yes are the real orientation. Your first projects are scoped by someone else. You learn to update them without drama and to ask for a decision while there is still time to use it. People who hide a slip until the review meeting are hard to promote, however strong the degree.

The next step is owning a project. Your name is on the status. You know the dependencies. You can tell a director, without a method lecture, whether the milestone is real. Some people stay individual contributors at that level for a long time and become the person a director trusts with the messy project. Others move into a lead role and spend more of the week on staffing, reviews, and the quality of other people's notes. Both are legitimate. Ask which one the company actually rewards, because some firms praise leadership and then pay the person who quietly owns the hardest project.

University paths fork toward more independent research, teaching support, or a move into industry once a grant ends. Company paths fork toward a larger program, a people-lead job, or a specialist role beside regulatory and manufacturing groups. When you compare those forks, compare the week, not the prestige of the noun in the title. A lead title that is all meetings and a staff scientist title that still owns a living project can be equally senior. Choose the week you can repeat for years.

May 2025 pay, from a broader engineering series

These figures are Occupational Employment and Wage Statistics for May 2025, published for Bioengineers and Biomedical Engineers, a series broader than a genetic-engineer title alone. Entry pay is $71,850. The national median is $109,370. The gap from entry to the median is $37,520. The high end of the published range in Massachusetts is $194,760, where the Bureau could report a high end because the workforce was large enough. The gap from the national median to that high end is $85,390. The Massachusetts figure is a range top. It is a different statistic from any state median.

State medians are typical pay, and they tell a different story from that Massachusetts range top. Arizona shows the highest median here, $141,230, which sits $31,860 above the national median. California's median is $128,310. Minnesota's is $127,730. Massachusetts, the same state as the range top, has a median of $127,570. Pennsylvania's median is $122,560. Texas shows the lowest median in these facts, $92,600. The gap between Arizona's median and Texas's median is $48,630. A recruiter who quotes $194,760 is quoting the high end of the published range in Massachusetts. A recruiter who quotes $127,570 is quoting typical pay in that same state. Those sentences should not be swapped.

Read your offer against the median first. A new graduate can expect a conversation that starts nearer $71,850 and moves toward $109,370 as the seat becomes real project ownership. An experienced engineer in Arizona should look at $141,230 before looking at a Massachusetts range top that does not travel with the job. An experienced engineer in Texas should look at $92,600 and at the national median of $109,370, and should notice that the state-median gap of $48,630 is large enough to matter if you are willing to move. None of these dollars are a private survey of genetic-engineer titles. They are the published series named once above, used as the best public picture we have for this kind of engineering work.

Using the gaps when the letter arrives

Write the base on a line by itself. Under it write $71,850, $109,370, and the state median if your state is one of the ones listed. If the job is in Massachusetts, also write $194,760 and label it the high end of the published range, then write $127,570 and label it the state median. If you mix those labels, you will argue about the wrong number. The $85,390 gap from the national median to the Massachusetts high end is the stretch at the far edge of the published range. The $37,520 gap from entry to the median is the stretch that matches a first real job growing into a solid one.

Negotiate the week along with the base. A seat that includes on-call manufacturing trouble, travel to another site, or supervision of other people is a different job from a seat that is status notes and a single project. If the base is already near a high state median, such as Arizona's $141,230, push on scope, support, and title rather than pretending the Massachusetts range top is the local going rate. If the base sits near entry while the posting describes a project owner, the $37,520 entry-to-median gap is the factual way to say the offer is priced like a start. You can say that without inventing a bonus survey.

Ask what is written beside salary: bonus target, equity if the company uses it, support for a graduate degree if you are still in school, and whether the city is firm. Then return to the published figures so both of you are looking at the same page. The degree got you in the conversation. The seat defines the week. The May 2025 numbers, used with the state median kept separate from the Massachusetts range top, keep the money talk from floating free of anything public.

The top of Genetic Engineer pay — and how to get there with AI

$194,760what Genetic Engineer pay reaches in Massachusetts

Highest state-level top-of-range annual wage for Bioengineers and Biomedical Engineers, among states with at least 500 people in the job. U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025.

And the role it leads to — Biochemists and Biophysicists — reaches $222,850 in Texas.

$71,850entry$109,370middle$194,760top end

In this job the top of the range belongs to whoever can move an experiment from bench to a defensible written record fastest, not to whoever runs the most constructs.

Count the written duties: technical reports, data summary documents, research articles for publication, regulatory submissions, patent applications, protocols and standards for equipment use and repair, project plans with timelines and capital spending requests, plus maintaining databases of experiment characteristics and results. That is most of a week, and almost all of it is assembling the same figures into different shapes for different audiences. The engineers who reach the top of the range stopped doing that by hand. They wrote the pipeline that pulls instrument output into a structured record once, then generates the summary, and they spend the recovered days on the recommendations about process formulas, instrumentation and equipment specifications that only a person who ran the pilot can make.

Your playbook, by where you are now

Just startingStructure the record before you automate it

  1. Fix one schema for your experiments — construct, host, conditions, run date, instrument, outcome, deviation — and use it for every run, even the failures.
  2. Keep construct and sequence records in ApE A Plasmid Editor rather than in file names, so a build history survives you leaving the bench.
  3. Export ADInstruments LabChart output into that schema on the day of the run, while you still remember why the trace looks odd.
  4. Get comfortable enough on Linux to run your own analysis rather than queuing for someone else's time.
  5. Read the trade and scientific literature for an hour a week and note anything that changes a specification you rely on.

What proves it: An experiment database complete enough that a colleague can reconstruct any run from it.

Realistic span: the first two or three years

A few years inWrite the pipeline, then check it hard

  1. Script the step that turns your raw run records into the weekly data summary, writing it with GitHub Copilot beside you and validating it against three summaries you already produced by hand.
  2. Emit the structured pieces of a submission in Extensible markup language XML so the same numbers do not get retyped into a report, a slide and a filing.
  3. Automate the plots, not the conclusions; a figure can be generated, an interpretation cannot.
  4. Draft the boilerplate of a protocol or standard for equipment maintenance with a model, then walk the actual equipment while you correct it.
  5. Put version control on the pipeline so a number in an old report can be traced to the code that produced it.

What proves it: A reporting pipeline in routine use, with a documented check against hand-produced output.

Realistic span: years four through eight

ExperiencedSpend the recovered time where judgment is paid for

  1. Take the recommendations nobody else can make: process formula, instrumentation choice, equipment specification, based on your own pilot results.
  2. Own the safety, efficiency and effectiveness evaluation for a line of biomedical equipment, and let the pipeline supply the evidence.
  3. Manage a team of engineers, running schedules, inventory and contract deadlines through the same record rather than a separate spreadsheet.
  4. Write the project plan and capital spending request for a facility improvement, with the throughput figures your automation produced sitting underneath it.
  5. Note where this work concentrates when you consider a move; Massachusetts carries the densest market for engineers in this field.

What proves it: A capital request or equipment specification approved on evidence your own system generated.

Realistic span: nine years onward

The next 90 days

Pick the single document you assemble most often — for most people that is the weekly or monthly data summary — and spend ninety days removing the assembly from it. Start by writing down every source it draws from and every transformation you apply by hand between the instrument and the page. Then build the smallest script that does the mechanical part, and run it in parallel with your manual process for six weeks, reconciling every discrepancy rather than assuming the script is right. Keep a record of the hours before and after. What you will hold at the end is not just a tool but an argument: here is a recurring cost the group was paying, here is what it now costs, and here is what I want to do with the difference.

Wage figures: BLS OEWS, May 2025. The playbook is PayCrunch editorial guidance, not a guarantee of pay or placement.

Careers related to Genetic Engineer

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 in your molecular-design platform, not a chatbot. Open Benchling (or SnapGene or Geneious) and turn on its AI-assisted design features for cloning, primer design, and CRISPR guide selection — this is where a genetic engineer's edits become real, versioned, and traceable. Let it propose guides and assembly plans; you verify off-targets and manufacturability.

For protein and sequence design, the University of Washington's RFdiffusion and ProteinMPNN are freely available, and the AlphaFold Server validates your designs for free. Use Claude or ChatGPT to write the Python that batches these tools and to explain unfamiliar assays — but keep proprietary sequences in approved, non-training environments.

The one rule, forever: Sequence-design AI is a starting point, not a validated construct — codon choices, off-target CRISPR sites, and predicted protein folds must be checked in silico (off-target scoring, AlphaFold self-consistency, screening for hazards) and then in the wet lab before anything is synthesized or expressed. Work under your Institutional Biosafety Committee, follow dual-use (DURC) review, screen all synthetic-DNA orders, and never use AI to design sequences with pathogen-enhancement or gain-of-function potential. Keep proprietary sequences out of consumer AI tools that may train on your inputs.
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
Design constructs and CRISPR edits with AI-assisted platforms
Why this pays: Fewer failed cloning rounds and cleaner edits mean faster DBTL cycles — the design velocity that gets your programs to milestones and you to lead-scientist comp.
BenchlingSnapGeneSynthego
1
In Benchling, design the assembly and use its CRISPR tools (or Synthego's design tools) to pick guides, sorting candidates by on-target score AND off-target risk, not just cutting efficiency.
2
Get the guide-selection and validation strategy right before you order.
Copy-paste this prompt
You are a CRISPR design expert. For a knockout of [gene] in [cell line/organism], walk me through selecting guide RNAs: which on-target and off-target scoring algorithms to trust for [Cas9/Cas12a], how to prioritize exons for a functional null, what PAM and GC constraints matter, and which controls (non-targeting, safe-harbor) I need. List the off-target validation I must do experimentally before trusting a clone.
Predicted off-targets miss real ones — confirm with GUIDE-seq or amplicon sequencing before any construct advances.
3
Version every design in the platform so each build is traceable back to its rationale and can be reproduced.
What you'll haveCleaner edits and fewer wasted cloning rounds — the cycle speed that moves programs forward and pay upward.
2
Design novel proteins and enzymes with generative models
Why this pays: De novo binders and engineered enzymes are the highest-value output in the field — the work that gets patented and pulls comp toward and past $195k.
RFdiffusionProteinMPNNAlphaFold 3
1
Generate backbones with RFdiffusion for a target (say, a binder to [antigen]), assign sequences with ProteinMPNN, then filter by AlphaFold self-consistency (predicted vs designed structure).
2
Plan a de novo binder campaign end to end before spending on synthesis.
Copy-paste this prompt
Help me plan a de novo binder campaign against [target protein, PDB/UniProt]. Outline the pipeline: hotspot residue selection, RFdiffusion backbone generation parameters, ProteinMPNN sequence design, AlphaFold2/3 filtering thresholds (pLDDT, PAE, RMSD to design), and how many designs to order for a first experimental round. List the wet-lab assays that confirm binding and the metrics that tell me a design failed.
In-silico self-consistency predicts success only weakly — order a diverse panel and let the binding assay, not the model, decide.
3
For enzymes, run the same loop with activity-focused filters, then test the top designs experimentally and iterate.
What you'll haveDesigned proteins and enzymes that actually work — patentable, program-defining output at the top of the band.
3
Optimize genes, codons, and expression in silico
Why this pays: Getting a construct to express and manufacture well is often the bottleneck between a good idea and a shipped product — solving it is directly rewarded.
BenchlingTwist BioscienceClaude
1
Codon-optimize for your host in Benchling or your synthesis vendor's tool (Twist Bioscience), and check for hidden restriction sites, repeats, and secondary structure before ordering.
2
Reason through what actually limits yield for your host.
Copy-paste this prompt
Act as an expression optimization expert. I'm expressing [protein] in [E. coli / CHO / yeast]. Given the CDS below, list the factors to optimize — codon usage and CAI, GC content, mRNA secondary structure near the start codon, ribosome binding site, avoiding cryptic splice sites and repeats — and flag anything in this sequence likely to hurt yield. Suggest a small set of ranked design variants to test. CDS: [paste].
Optimization heuristics conflict — synthesize a small ranked panel and let titer data pick the winner rather than trusting one predicted 'best' sequence.
3
Order a small variant panel and let the titer and quality assay choose the winner, then standardize it.
What you'll haveConstructs that express and manufacture — turning designs into products, which is the value that pays.
4
Run a data-driven design-build-test-learn loop
Why this pays: The engineer who runs a tight, data-driven DBTL loop iterates faster than peers — the productivity that earns program leadership and comp.
ClaudeJMPBenchling
1
After each build-and-test round, load your results into JMP for design of experiments and use an LLM to decide what to vary next — moving beyond one-factor-at-a-time.
2
Turn last round's data into a formal next-round design.
Copy-paste this prompt
You are a design-of-experiments and strain-engineering expert. Here are results from my last DBTL round: [variants, factors changed, measured outputs]. Identify which factors drove [expression/activity/yield], propose the next round as a formal DoE (factors, levels, design type, number of runs), and predict which combinations are most promising and why. Flag confounded factors and anything the data cannot distinguish.
AI ranks hypotheses but can't see your assay noise — replicate top candidates and confirm effects are real before committing a full campaign.
3
Track every round in Benchling so the learn step compounds instead of restarting each time.
What you'll haveA faster, smarter DBTL loop — more wins per quarter and the track record to lead a program.
5
Automate analysis and become the team's computational engineer
Why this pays: The genetic engineer who can script NGS and screen analysis and build shared design tools becomes indispensable across projects — the route into senior and lead comp.
ClaudeCursorBenchling API
1
Use Cursor or Claude to write Python that parses your amplicon and NGS editing data (for example CRISPResso2 outputs) and pulls designs from the Benchling API automatically.
2
Generate a reusable editing-analysis script.
Copy-paste this prompt
Write a Python script that takes a folder of CRISPResso2 output for an editing experiment and produces a summary table per sample: percent edited, percent knockout (frameshift), top indel patterns, and a flag for samples below [threshold] editing. Handle missing files gracefully and output a tidy CSV plus a bar plot. Add comments a wet-lab engineer can follow.
Spot-check the script's numbers against a couple of samples by hand before trusting it across a whole plate.
3
Share the scripts and teach the team; owning the analysis layer is what makes you indispensable.
What you'll haveThe person every project relies on for design and analysis — the indispensability behind top-of-range offers.
Your 12-month sequence to the top of the range

How the plays above stack into a path from median pay toward the $194,760 tier.

Month 1
Turn on AI-assisted design in Benchling or SnapGene and run one edit or clone with AI-picked guides you validate for off-targets.
Months 2-3
Add generative protein design (RFdiffusion, ProteinMPNN, AlphaFold filtering) to one real target and test the top designs.
Months 3-6
Tighten your DBTL loop with design of experiments and LLM-planned rounds, and script your NGS and screen analysis.
Months 6-12
Ship a de novo design win or a hard expression fix, plus a shared analysis tool the team uses.
Year 2
Lead a program's design strategy and the biosafety and QA of AI-designed sequences — the lead-scientist scope behind top pay.
Next steps for a Genetic Engineer

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.

Genetic Engineer work is specific enough that a stamped 'check out these courses' block would be noise. BLS files this work as Bioengineers and Biomedical Engineers (SOC 17-2031). 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 Engineering and Technology and Physics; the links search those subjects, not a generic 'career courses' list.

Genetic Engineers in this dataset list Autodesk AutoCAD among the tools in use, so a program that names that stack is a better fit than a survey course.

Engineering And Technology programs on Coursera for Genetic Engineer work

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

Engineering And Technology courses on edX

edX search for engineering and technology, aimed at engineering (SOC 17-2031). Same field as the Coursera link, different university catalog.

Screened remote and flexible Genetic Engineer 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 Genetic Engineer work, not a claim that they list a counted SOC 17-2031 inventory.

Build a Genetic Engineer resume on Resume Now

Write a Genetic Engineer resume, or one aimed at Biochemists and Biophysicists, instead of a blank template. Resume Now is a resume builder; we are not claiming a counted template set for this SOC.

Build a Genetic Engineer resume on Zety

A Genetic Engineer resume that names the actual tasks on this page, or the step-up title Biochemists and Biophysicists, beats a blank template when you apply.

What Genetic Engineers earn by state

These are the Bureau of Labor Statistics’ own figures for Bioengineers and Biomedical Engineers, 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.

Arizona
$141,230
highest of them · +29% vs the national median
Texas
$92,600
lowest of the 13 states that qualify · -15% vs the national median
The same job pays $48,630 more a year at the median in Arizona than in Texas — 53% higher. That gap is what the Bureau measured, before any question of what it costs to live in either place. The top-of-range figure quoted at the head of this page, $194,760, is a different statistic in a different place: it is the 90th-percentile wage in Massachusetts. The state that pays the typical worker most and the state where the best-paid go highest are not always the same one.
Arizona$141,230California$128,310Minnesota$127,730Massachusetts$127,570Pennsylvania$122,560New Jersey$114,680Ohio$111,550Indiana$108,030

Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025, SOC 19-1021. 13 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.

Frequently asked
Will AI replace genetic engineers?
No — it automates the design half of the loop, not the build and test. Someone still has to run the wet lab, judge which AI design is manufacturable and safe, read failed assays, and own biosafety and IP. Engineers who master generative design pull ahead; those who only clone to a protocol are the most exposed.
Can I trust AI-designed sequences and guides?
As candidates only. Off-target predictions miss real cuts, de novo designs mostly fail experimentally, and codon optimizers disagree with each other. Verify in silico, then always in the wet lab with proper controls, before any design advances.
What are the safety and dual-use rules?
Work under your Institutional Biosafety Committee, follow dual-use (DURC) review, and never use AI to enhance pathogens or design hazardous sequences. Screen all synthetic-DNA orders. AI lowers the barrier to dangerous design, which makes human accountability stricter, not looser.
Is it safe to paste our sequences into ChatGPT?
Not proprietary ones into consumer tiers that may train on inputs. Use enterprise or zero-retention settings, or local models, for IP-sensitive sequences, and public tools for public data and code. Check your data-use and IP policy first.
How does AI raise my pay?
By compressing design cycles and unlocking high-value output — de novo binders and enzymes, hard expression problems solved, tighter DBTL loops, shared tools. Faster wins on harder problems, plus visible ownership, is what moves you into lead-scientist comp.
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