Last updated: June 22, 2026 | By Gregg Flecke

Should I Hire a Full-Time AI Engineer or a Contractor? (Decision Framework)

Hire a contractor when the work is a defined project, the scope is still fuzzy, or you need someone shipping in days. Hire full-time when you need ongoing model ownership and institutional knowledge. When you genuinely can’t tell, contract-to-hire settles it.

That’s the version that fits on a sticky note. The one that actually saves you money runs a little longer, and it turns on a number most hiring managers get backwards before we even talk.

I’m Gregg Flecke. I’ve spent close to 30 years placing technical talent, contract, direct hire, and project-based, for everyone from 40-person insurance startups to Fortune 500 financial firms. The full-time-or-contractor question lands on my desk a few times a week now, ever since every company with a product and a pulse decided it needed an AI roadmap and a confident answer for the board about how fast it’s all moving. The reason people ask out loud is almost always cost. They’ve heard contractors are cheaper. So they call to confirm it.

They’re usually wrong. Not because contractors are expensive, but because the comparison they’re running in their head isn’t the comparison that matters. Fair disclosure on my seat in this. KORE1 places both contractors and full-time engineers, so we get paid whichever way you go. No thumb on the scale, which makes it an easy one to be straight about. If you want the wider view of the function, we keep an AI and machine learning engineer staffing practice, and this piece sits underneath it.

The cost comparison nobody runs correctly

The break happens in one specific spot. A hiring manager sees a contractor billing $160 an hour next to a full-time engineer asking for $200,000, runs some fast mental math, and decides the salary is the bargain. Or they flip it. The contractor’s hourly looks scary, the salary feels like a fixed cost you can plan a year around, so the contractor reads as the splurge. Both are guessing.

Both versions skip the part that changes the answer. A salary is not what a full-time employee costs you. Not even close.

The Bureau of Labor Statistics tracks this directly. As of December 2025, benefits make up 29.9% of total compensation for private-industry workers, the rest being wages. Health insurance, the employer side of payroll taxes, paid leave, retirement match, unemployment insurance, workers’ comp. None of it shows up on the offer letter. All of it shows up on your books.

For a $200,000 AI engineer that 30% isn’t a clean multiplier, to be fair. Health premiums don’t scale with salary, so the benefit share actually shrinks at the top of the pay scale. Call it 1.25 to 1.3 for a senior role. So your real annual cost lands somewhere near $250,000 to $260,000. And that’s before the recruiter fee, before the three-month ramp where they’re learning your codebase instead of shipping, before equity, before the cost of getting the hire wrong. The offer letter said $200,000. Your finance team is writing bigger checks than that.

Now do the contractor. A strong senior applied AI engineer bills $150 to $200 an hour in most U.S. markets, going by ZipRecruiter’s contract benchmarks. Say $160. Run that across a full year of full-time hours, about 1,800 billable, and you land at $288,000. Nothing loaded on top, no payroll tax, no premium for the health plan. And it’s still more than the employee once you do the comparison honestly.

That number surprises people. Sit with it. Same year, same hours, like for like, and the contractor is the more expensive choice, not the bargain. I’ve watched CFOs do a double take on that one. More than once. The reflex that says “a contractor will save us money” is just wrong more often than it’s right.

So why does half the AI hiring still go contract? Because that like-for-like comparison is a fiction nobody actually lives. Hardly anyone needs one senior AI engineer, full-time, for twelve straight months, on a problem they’ve already pinned down to the spec. That last bit is the whole game. Right there. It’s where the real decision lives, and the salary-versus-hourly argument walks straight past it without ever slowing down.

One more wrinkle on that full-time number: the sources can’t agree on it. Levels.fyi pegs the median AI engineer near $151,000. Glassdoor’s senior figure runs all the way to $285,000, though that number is dragged up by equity-heavy firms in San Francisco and Seattle. Most mid-market employers I work with land in between, call it $185,000 to $260,000 base. The spread itself is the lesson. A wide one, at that. Benchmark the comp against your own market before you commit, because a national blend that mixes Bentonville salaries with Palo Alto equity packages hands you an average that describes no actual company you will ever hire from.

Five questions that actually decide it

Cost sets the frame. These settle the call. Work down them and stop at the first one that answers cleanly, because they’re roughly ordered by how often each turns out to be the deciding factor.

Is this a project or a capability?

A project ends. “Build a retrieval pipeline on AWS Bedrock and Pinecone, wire it into the support tool, hand us the eval harness.” You can hear the finish line in that sentence. A capability has no such line. “Own how AI shows up across the product for the next three years” is a seat, not a sprint. Projects lean contract, capabilities lean full-time, and this single question sorts maybe half the calls I take, usually before we have even gotten around to talking comp, because once you know whether the work has an end date the rest of the decision starts arranging itself.

Do you honestly know what you need yet?

The expensive question, this one. If you can’t put the first project into one plain sentence, then hiring a permanent engineer means paying a $255,000 loaded salary to go discover your own requirements. Pricey way to write a spec. A contractor buys that same clarity for a sliver of the commitment, and you hire for keeps once the role finally has a shape.

How fast does someone need to be shipping?

Days, if it’s a contractor. Our direct IT searches average about 17 days to a signed candidate once the spec is tight, and a senior AI search can hit that too. Then the notice period lands on top. Just like that, “we need this now” is 6 to 8 weeks from the first line of committed code. Board wants something to demo this quarter? That gap makes the decision for you.

Will the work create knowledge you can’t afford to lose?

Some AI work is scaffolding you’ll tear down next sprint. Other work becomes the spine of the product. The prompts, the evals, the data contracts, the hard-won feel for why the model goes sideways every Tuesday afternoon. When that second kind walks out with a departing contractor, you didn’t save a dime. You rented your own foundation and handed it back.

Is the money headcount or project budget?

Unglamorous question. It quietly decides more of these than anyone likes to admit. Headcount reqs move at the speed of annual planning, and project dollars move this week. Plenty of teams go contract for no grander reason than that being the budget they can release right now. Nothing wrong with that. Just say it out loud, so the choice is a choice and not an accident.

Not one of those turns on the hourly rate. Funny how that works. The rate is the least interesting number in the entire decision.

When a contractor is the right call

Defined scope with a real end date. That’s the cleanest case. You need a recommendation engine prototyped, a fine-tune run on a dataset you already have, an eval framework stood up so your existing team can stop grading model output by hand. Bring someone in. Ship it. Wind it down.

Uncertainty is the other big one. A fintech client last year was convinced they needed a permanent ML team. We placed one senior contractor for a quarter to pressure-test the idea first. Turned out the actual problem was a data plumbing mess, the sort of thing a single data engineer could untangle in a month, and the permanent ML team they were ready to staff up and pay for could wait another six months without anyone feeling the delay. One contractor saved them from building the wrong team. Cheapest mistake they never made. Contract staffing earns its keep most when the question is still “what do we even need,” not “we know exactly what we need and want to pay more for it.”

Speed is the third. App Store deadline, investor demo, a competitor who just shipped the thing your CEO saw on a podcast. When the clock is the constraint, a contractor billing next week beats a perfect full-time hire who starts in two months. Every time. No contest.

And there’s the specialist you’d never keep busy. A computer-vision expert for one medical-imaging feature. Someone who’s actually fine-tuned open-weight models at scale, for a single hard problem. You can’t fill 40 hours a week of that work, year-round. So don’t try. Rent the expertise for the slice you actually need, and let them go when the feature ships.

When you need a full-time AI engineer

Flip every one of those and you get the full-time case.

The work is ongoing and central. AI isn’t a feature you ship once and forget, it’s quietly becoming the way your product prices things, ranks things, answers people, and flags the weird stuff, and somebody has to own all of that machinery for years, not weeks. Model stewardship is real work. Unglamorous, and never actually finished. Retraining as data drifts. Watching costs as usage scales. Fielding the 11pm page when the model starts hallucinating refund amounts. A contractor doesn’t carry that. An owner does.

You also go full-time when the knowledge has to compound. The engineer who built your eval suite is the only person alive who knows why each threshold sits exactly where it does, why that one ugly retry loop exists, and which 2am incident taught you both to add it in the first place. That context is worth more in year two than in year one. It only accrues if the person stays. Tech demand backs this up structurally, by the way. BLS projects 15% growth for software developers through 2034, roughly five times the average across all jobs, with about 129,200 openings a year. The good ones have options. If your AI work is core, you want them on your team, not on someone’s bench between gigs.

And culture, quietly, matters. A full-time engineer interviews the next hire, mentors the junior, pushes back in the architecture review because they’ll live with the consequences. That ownership reflex is hard to contract for. Building a team, not just a feature? Hire one. We handle that side through direct hire staffing, and it’s the right tool when the answer to that first question came back “capability.”

The path most teams should look at first

There’s a third door, and it’s the one I point most clients to when they’re genuinely torn: contract-to-hire.

You bring the engineer on as a contractor for a defined window, three to six months is the usual shape, with the conversion to full-time already negotiated and sitting in a drawer in case the work and the person both turn out to be everything you were hoping for. You get the fast start. You get the low-commitment exit, too. And you buy yourself a few months to find out whether this person can actually own your AI work before you ever hand them a salary and equity. And they get to find out whether your problem is interesting and your data is real before they quit a job for you.

It resolves the two scariest variables at once: is this the right person, and is this even the right role. For a first AI hire especially, where a mis-hire can cost six figures and a quarter of lost momentum, that trial period is cheap insurance. If you’re hiring your very first AI person, our guide to hiring your first AI engineer walks through the readiness checks that should come before any of this. And the broader trade-offs across every role live in our breakdown of contingent workforce versus full-time.

Contract-to-hire isn’t free, to be clear. There are conversion fees on the back end, and a contractor who knows the whole thing is secretly an audition tends to carry themselves a little differently than one who’s just there to do a job and leave. But for the team that keeps flip-flopping between the two columns of that table up above, it’s usually the honest answer. Try first. Then commit.

What hiring managers actually ask us

Is a contractor actually cheaper than a full-time AI engineer?

Usually not, on a full-year basis. A senior contractor at $160 an hour runs about $288,000 across a year of full-time hours, more than a $200,000 employee even after you load roughly 30% in benefits and taxes. Contractors save money only when you don’t need them year-round. Which is most of the time.

If we go contract, can we keep what they build?

You can, but only if you build it into the deal. Put IP assignment in the contract, and make documentation and a knowledge transfer actual deliverables rather than afterthoughts. The real risk was never ownership on paper. It’s the undocumented context living in one person’s head that leaves the day the contract ends.

How fast can you get an AI contractor started versus a full-time hire?

Days to two weeks for a qualified contractor. A full-time hire is more like 6 to 8 weeks once you add sourcing, interviews, an offer, and the candidate’s notice period. Our IT searches average about 17 days to a signed candidate, and notice is on top of that.

Does contract-to-hire actually work for AI roles?

More often than people expect. A three-to-six-month trial lets you confirm the person can own the work and that the role is even real before you commit salary and equity. For a first AI hire, where mistakes run into six figures, it’s the lowest-risk path we offer.

We’re a startup with no AI people yet. Which way should we lean?

Lean contract, at least to start. Until you’ve actually shipped one real AI feature into production where customers touch it and something breaks at 2am, you’re still learning what the role even needs, and a permanent hire just locks in a guess you aren’t ready to make. Use a contractor or contract-to-hire to define the work. Convert once the seat is clear.

Do we even need an AI specialist, or just a good engineer who knows the tools?

Sometimes the second one. If the job is wiring an existing model API into your product, a strong senior software engineer comfortable with AI tooling can handle it, faster and cheaper than a specialist. Save the specialist for problems that genuinely require depth, like fine-tuning or novel architecture.

The honest bottom line

Match the model to the work, not to whichever number looks smaller on the invoice. A defined project with an end date, a scope you’re still mapping, or a deadline a normal search has no chance of beating, those all point toward contract. When AI is becoming something your product will lean on for years and someone has to own it, you’re looking at a full-time seat. And if you keep sliding back and forth between the two, quit deciding in your head and let contract-to-hire settle it over a few months of real work.

Nearly every team I’ve watched get this wrong made it about budget when it was really about scope. Work out what the job actually is. How long it lasts. Who has to live with whatever gets built. The money question tends to sort itself out once those three are clear.

A second read on which way your particular role leans is the free part of what we do. Talk to a recruiter on our team and you’ll get it straight, including the times the honest answer is that you don’t need us for this one. Nearly 30 years placing both contractors and full-time engineers, and a 92% client retention rate riding on whether that kind of advice holds up. We’d rather be right than busy. Always have been.