| Company and role | Base salary | Experience | Where |
|---|---|---|---|
| Gusto Enterprise Application AI Engineer | $185k to $205k (Denver); $225k to $250k (SF) | 8+ years | SF hybrid |
| Asana Senior Software Engineer, AI Retrieval | $202k to $223k | 6+ years | NYC |
| Asana Software Engineer, AI Developer Experience | $171k to $190k | 3+ years | NYC |
| Brex AI Engineer, Product | $171k to $240k | Not stated | SF hybrid |
| Mercury Senior Software Engineer, AI | $166.6k to $218.7k | 5+ years | SF / NYC |
| Lyft Senior AI Software Engineer, Risk | $148k to $185k | 6+ years | SF / Seattle |
| Vercel Software Engineer, AI SDK | $196k to $294k | 5+ years | SF hybrid |
| Coinbase Senior SWE, AI Platform | $186k to $218.9k | 5+ years | Remote US |
| Harvey Senior SWE, AI Platform | $185k to $325k + equity | 5+ years | SF |
| Harvey Sr. AI Enablement Engineer | $133.5k to $200.3k + equity | 5+ years | Remote |
| Ramp Applied AI Engineer | $204.4k to $352k + equity | Not stated | NYC hybrid |
| OpenAI Applied AI Engineer, Enterprise | $197k to $278k + equity | Not stated | SF hybrid |
Read from the companies’ own job boards on 14 August 2026. US base salary; equity noted where the posting notes it. Harvey appears twice on purpose.
Post a job called “AI engineer” this year and the inbound is enormous. Half the CVs carry the title in the first line. And after a dozen calls, most founders still can’t tell which of these people has ever put a language model in front of a paying customer.
That’s the actual problem, and it isn’t a shortage. It’s a title that means at least four different jobs, applied to a market where every engineer with a weekend project can honestly claim it.
So this is a guide to telling them apart, and to not overpaying for the wrong one. The table above is what the job pays right now at companies that publish their bands. Below is what those companies actually ask for, which turns out to be more useful than the title.
There’s a lazy way to read a job called “AI engineer”, which is to assume it wants someone who builds models. That’s mostly wrong, and the postings say so if you read past the headline.
We pulled the full text of 83 current AI engineering postings from companies that don’t make models themselves: Stripe, Figma, Coinbase, Gusto, Asana, Brex, Vercel, Lyft, Datadog and the like. These are the employers most readers of this article resemble. What they ask for is lopsided in an instructive way.
Read that again from the bottom. Nine percent. The thing most people picture when they hear “AI engineer” is a requirement in one posting out of eleven.
Brex puts it in writing. Its “AI Engineer, Product” listing is for someone who will build an agent that audits customer spend. It puts “experience building products on top of LLMs or agentic systems” under Bonus points. Not requirements. The requirements are the ones you’d write for a senior product engineer.
A company hiring an AI engineer to build an agent considers agent experience a bonus. That is the whole role in one line.
The AI engineer most startups need is a strong product engineer who has shipped one thing on top of a model and can tell you what broke.
The bands in the table run from under $150,000 to over $350,000, and that spread isn’t a market disagreement. It’s several jobs sharing a name.
Across 158 AI engineering titles at ten AI-native companies, they sort into four families. Product or applied engineers build the feature. Platform engineers build the routing, serving and infrastructure the feature runs on. Enablement, deployment and support engineers help customers get value from what was built. And forward deployed engineers do the building inside a customer’s own systems, which we wrote about separately. Every one of them is an AI engineer on LinkedIn.
Harvey, the legal AI company, has both a Senior Software Engineer, AI Platform at $185,000 to $325,000 and a Senior AI Enablement Engineer at $133,500 to $200,300 open at the same time. Same word, same company, same seniority.
One builds the model routing and agent architecture the product runs on. The other helps customers get value from it. A hundred thousand dollars lives inside the word “AI”.
For a startup this matters in a specific way. If your posting says “AI engineer” and nothing else, you’ll get applicants from every one of those buckets, and you’ll pay the one you accidentally hire the rate of the one you were picturing.
Say what the person will own. “You will build the retrieval and eval pipeline behind our support assistant” filters better than any list of frameworks.
And keep the premium in proportion. Stack Overflow’s 2025 survey puts the US median total compensation for AI/ML engineers at $189,500 against $175,000 for back-end developers, across 5,239 US respondents. That’s about eight percent.
Real, but nowhere near the doubling that the panic around this title implies. You’re paying for a product engineer with one more skill, not for a different species.
Here’s the split that matters, and it isn’t fine-tuning versus prompting.
The engineers worth hiring have been through the part where the demo works and the product doesn’t. They talk, unprompted, about evals: how they measured whether the thing was getting better, what the test set looked like, what they did when a model update broke a case that used to pass. They know what an LLM call costs at scale and what it does to a page’s latency budget. They have a story about a hallucination that reached a user.
The postings ask for exactly this. Figma’s London role wants “experience curating and developing eval sets and LLMs as judges.” Gusto lists “robust evaluation frameworks (evals), guardrails for code safety, and strategies for optimizing model latency and token spend.” Stripe screens for people who’ve built agents “beyond basic prompt engineering.”
The ones to be careful with have a portfolio of things that work in a screenshot. A wrapper around a chat completion. A retrieval demo over one PDF. They can talk about agents fluently and haven’t run one for a month.
None of this is a knock on them. Eighteen months ago almost nobody had production experience, and the fastest way to get it is to be hired without it. But you should know which one you’re paying for.
Forget the whiteboard algorithm round. It tests something you already tested by reading the CV.
Ask for a war story, then pull the thread. “Tell me about an LLM feature you shipped that behaved differently in production than in testing.” Anyone who’s done the work has three. Then: how did you find out? What did you measure? What did you change? What did it cost? A candidate who has never been paged for a model regression will be answering in generalities within two questions.
Give them a real, small piece of your problem, and pay for it. Ten hours, your data, your constraint. Not “build a chatbot” but “here are 200 support tickets and our current classifier’s mistakes; make it better and show me how you know it’s better.” What you’re grading isn’t accuracy. It’s whether they built a way to measure, whether they can explain the trade-offs, and whether they noticed the tickets are messy. We wrote up how to run this in our guide to the paid trial project, and it applies here without changes.
Ask about the money. “This runs on a frontier model at 40,000 requests a day. What’s the bill, and what would you do about it?” You’re not testing arithmetic. You’re testing whether they’ve ever had to care.
If you’re in San Francisco or New York and hiring in-house, the table above is your market, and there’s not much to add to it.
Except that the price has a shape. It’s a US in-office price, and ten of the twelve postings in the table are hybrid or on-site, for a job that, more than most, doesn’t need an office. The stack is a laptop and API keys.
The talent has already moved. Stanford’s 2026 AI Index reports that in 2025 a larger share of job postings in Poland required AI skills than in the United States, 2.92 percent against 2.6, with Chile close behind at 2.4. On the supply side, LinkedIn’s data in the same report shows AI talent concentration between 2019 and 2025 growing 111 percent in Portugal, 107 percent in Brazil, 82 percent in Chile and 76 percent in Argentina.
The same seniority on a remote contract from those markets runs in the $50 to $80 an hour range that senior contractors bill generally. Notice what that number doesn’t have: an AI surcharge. Of the platforms that publish a client rate at all, none publishes a separate one for LLM work. In Bay Area base salaries the premium exists; on the contract market it hasn’t been priced in yet. It’s mostly unused because founders don’t know how to screen for the work remotely.
You screen for it the same way. The war story doesn’t care about time zones. The paid trial works better remotely, if anything, because it’s how the person will actually work.
If you’re adding AI to an existing product and you have one hire, don’t hire a researcher. Hire the product engineer described above and give them a budget for API calls. If the feature works, the second hire can be someone who goes deeper on models. Hire the researcher first and you’ll get a very good notebook and no product.
If you’d rather not build this interview loop yourself, it’s roughly what we run before a client sees a profile: the war story, the paid trial on real work, the question about the bill. Tell us what you’re building and the people you meet will already have answered all three.
Privacy is important to us, so you have the option of disabling certain types of storage that may not be necessary for the basic functioning of the website. Blocking categories may impact your experience on the website. More information
