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AI Salary Explorer
A quick, transparent estimate of total-compensation ranges for AI and software roles in 2026. Pick a role, level and company tier and it shows an indicative band, built from a public base rate and clearly-labelled tier and level multipliers rather than a black box. Calibrated so the anchors match reported figures — a senior big-tech AI engineer around the low-$200Ks, a frontier-lab senior in the high-six-figures. These are self-reported market ranges for orientation and negotiation prep, not an offer or advice — always verify live on a source like Levels.fyi.
The company tier is the biggest lever — total comp is equity-dominated at the top, so a frontier lab can pay multiples of a big-tech number for the same role. AI-specific roles carry a reported ~50%+ skills premium, encoded in their higher base rates.
A salary band is only as trustworthy as the model behind it, so this explorer shows all of its working. Instead of a table of numbers with no provenance, it builds every band from three visible factors: a base rate for the role, a multiplier for the level and a multiplier for the company tier. You can see exactly why a number is what it is, and you can see what the model leaves out.
What it computes
All figures are indicative total compensation in thousands of dollars a year. The widget describes them as self-reported market ranges from a transparent model, last reviewed in July 2026.
midpoint = base[role] × level[level] × tier[tier]
low = round(midpoint × 0.88)
high = round(midpoint × 1.12)- base[role] is the senior, big tech figure for the role.
- level[level] scales it for seniority.
- tier[tier] scales it for the kind of company.
- The band is a fixed 12% either side of the midpoint, and all three outputs are rounded to whole thousands.
| Input | Options and values |
|---|---|
| Role base ($K) | Software Engineer 240, AI/ML Engineer 260, Research Engineer 300, Applied Scientist 320, Data Engineer 210, MLOps / Platform 230 |
| Level multiplier | Mid 0.72, Senior 1.0, Staff 1.45, Principal 1.9 |
| Tier multiplier | Traditional enterprise 0.55, Well-funded startup 0.9, Big Tech 1.0, Frontier lab 2.8 |
A worked example
The explorer opens on an AI/ML Engineer at Senior level in a Big Tech company.
- Base rate for AI/ML Engineer:
260. - Senior multiplies by 1 and Big Tech multiplies by 1, so the midpoint is
260 × 1 × 1 = 260, shown as midpoint ≈ $260K. - Low edge:
260 × 0.88 = 228.8, rounded to $229K. High edge:260 × 1.12 = 291.2, rounded to $291K. - The band reads $229K to $291K, and the breakdown panel lists base $260K, ×1 for level, ×1 for tier, and the $260K midpoint.
Keeping the role and level fixed and changing only the company tier shows which lever matters most:
| Company tier | Multiplier | Midpoint | Band |
|---|---|---|---|
| Traditional enterprise | 0.55 | $143K | $126K to $160K |
| Well-funded startup | 0.9 | $234K | $206K to $262K |
| Big Tech | 1.0 | $260K | $229K to $291K |
| Frontier lab | 2.8 | $728K | $641K to $815K |
Changing only the level at Big Tech gives $165K to $210K at Mid, $332K to $422K at Staff and $435K to $553K at Principal.
Assumptions and limits
- The factors are independent. The same level multiplier applies at every company and for every role, and the same tier multiplier applies at every level. Real ladders are not that tidy.
- The band width is fixed. Plus or minus 12% is a convention of the model, not a measured spread, so it is the same for a mid-level enterprise role and a principal frontier-lab role.
- No location, no equity terms. There is no city or country input, and no split between salary, bonus and stock, so vesting schedules, refreshers and the risk in private-company equity are invisible.
- The role premium is modest. The widget's note cites a reported AI-skills premium of about 50% or more, but its own base rates put AI/ML Engineer 8%, Research Engineer 25% and Applied Scientist 33% above Software Engineer.
- It is a snapshot. The ranges were last reviewed in July 2026 and markets move, especially at the frontier-lab end.
How to use the result
- Sanity-check an offer first. Set the role, level and tier that match the offer. Below the low edge is a reason to ask how it was built; inside the band, the conversation moves to level, equity and sign-on.
- Argue about level before money. Senior to Staff is a 45% step in this model, larger than any difference between roles, so being leveled correctly is usually worth more than haggling inside a band.
- Compare tiers honestly. A startup band 10% under big tech can still be the better offer, or the worse one, depending on what the equity is really worth.
- Bring live data to the negotiation. Use the band for orientation, then bring current comparables for the exact company and level, for example from Levels.fyi.
How it works
- Midpoint = role base rate × level multiplier × company-tier multiplier.
- The band is the midpoint ±12%, rounded to the nearest $1K.
- Company tier is the biggest lever: 0.55× (enterprise) to 2.8× (frontier lab).
- Orientation and negotiation prep, not an offer or advice; last reviewed 2026-07.
Frequently asked questions
Where do the salary numbers come from?
They come from a small, visible multiplier model rather than a scraped table. Each role has a base rate in thousands of dollars of total compensation at a senior level in a big tech company. That base is multiplied by a level factor and a company-tier factor to give a midpoint, and the band runs 12% either side of it. The widget describes the figures as indicative, self-reported market ranges, last reviewed in July 2026. They are for orientation, not a quote.
What does a senior AI/ML engineer at a big tech company make in this model?
The default view answers exactly that. The AI/ML Engineer base rate is $260K, and the Senior and Big Tech multipliers are both 1, so the midpoint is $260K and the band is $229K to $291K. Moving the level changes it the most within one company: Mid gives $165K to $210K, Staff gives $332K to $422K, and Principal gives $435K to $553K. All of these are total compensation, not base salary alone.
Why does company tier change the band so much?
The tier multipliers run from 0.55 for a traditional enterprise to 2.8 for a frontier lab, a spread of about five times, which is far wider than the gap between roles. The widget explains why: total compensation is dominated by equity at the top of the market, so a frontier lab can pay multiples of a big tech number for the same job. For a senior AI/ML engineer the band moves from $126K to $160K at an enterprise up to $641K to $815K at a frontier lab.
Do AI roles pay more than software engineering roles?
In this model, yes, but by less than the headlines. The widget's note cites a reported AI-skills premium of about 50% or more, while its own base rates are more conservative: AI/ML Engineer is 8% above Software Engineer ($260K against $240K), Research Engineer is 25% above, and Applied Scientist is 33% above. Data Engineer and MLOps or Platform sit slightly below. Level and company tier move the band much further than the choice between these roles.
Does the band include equity, bonus and location?
The band is total compensation, which normally means base salary plus bonus plus the yearly value of stock grants, but the model does not split those parts out. It has no location input and no equity terms, so it cannot reflect a city, a vesting schedule, a refresh grant or the risk that private-company stock is worth less than its paper value. Those are exactly the details that make two offers with the same headline number very different.
Can I use this salary band to negotiate an offer?
Use it to calibrate before you respond, not as the number you quote. If an offer lands below the low edge of the band for its role, level and tier, that is a reason to ask how it was built. If it lands inside, the conversation is about specifics such as level, equity and sign-on. For the negotiation itself, bring live comparables for the exact company and level, for example from Levels.fyi, because a general model is easy to dismiss.