Data & AI Salary and Job Market in the US 2026

You are job hunting in data or AI in the US and you want to know what the market looks like right now and what you can realistically ask for. Here is the picture from our listings as of 14 October 2026, plus the pay norms and negotiation moves that matter.

The market as of 14 October 2026#

On 14 October 2026, the JobRise live jobs feed (jobrise.io) shows 519 open Data & AI jobs in United States. Of those, 305 were posted in the last 30 days and 147 in the last 7 days. These are counts of open listings, not a market survey, so read them as a snapshot of what is being posted, not as a measure of total employment.

These are live counts and they move. If you are reading this later, check the current numbers before you plan around them. Numbers shift daily.

Who is hiring#

The most open listings sit with a small set of employers. Anthropic has 65, Scaleai 36, Faire 19, Lyft 18, Datadog 18, Pinterest 18, Figma 17, Airbnb 16, Klaviyo 16, and Brex 13.

Some of these names are recruiters or staffing firms rather than the end employer, so read the listing carefully, because a role posted by a staffing firm can be at a company you have never heard of and the pay and benefits can differ from the brand name on the posting. That matters more than you think.

The work is concentrated in a few metros. San Francisco has 175 listings, New York 105, Boston 25, Seattle 9, and Denver 8. If you are open to those cities you have far more to apply to than if you are restricting yourself to one remote-friendly market.

Nearly every listing is full time: 514 full-time, 4 part-time, and 1 contract. Contract data work in the US usually pays a higher hourly rate and no benefits or paid time off, so compare the total package, not the headline number.

What the roles actually are#

The spread is wide. Recent examples include "AI Business Transformation Lead" at City Wide Facility Solutions, "Member of Technical Staff - Post-training (Data Infrastructure)" at Cohere, "Private Markets Secondary Data & AI Vice President" at Neuberger Berman, "Senior Data Scientist, Audio" at Deepgram, "Business Intelligence Analyst" at Carrot, "Data Analyst" at Spectrum Comm Inc, "Policy Development, Technical AI Governance" at Anthropic, and "Senior Director of Real Estate Financial Modeling and Capital Analytics" at Harmony Homes.

Titles are not standardized. "AI Solutions Strategist" and "Machine Learning Engineer" can sit at the same level and pay very differently, so match on the responsibilities and the level, not the words in the title. Read the scope, not the label.

Entry-level openings are rare. Only 2 of 519 listings look like intern, graduate, junior, or trainee roles, for example "AI Research Engineer, New PhD Graduate" at Duolingo and "Junior Quant - PM Analytics" at Man Group. If you are early career, plan to apply more broadly and lean on projects and internships to get past the first filter. That's the catch. Apply anyway.

What listings actually pay#

Only 15 of 519 listings show pay at all. That is the single most useful fact in this whole article: the vast majority of US data and AI postings do not tell you the number up front, which means you will be guessing unless you ask. Brutal, but true.

The salaries that are posted, quoted exactly as shown:

• "Member of Technical Staff - Post-training (Data Infrastructure)" at Cohere: CA$295K, CA$535K • Offers Equity • Multiple Ranges
• "Senior Analyst, Employee Lifecycle Data (Workday)" at Ramp: $90K, $120K • Offers Equity
• "Machine Learning Engineer" at Ramp: $200K, $330K • Offers Equity
• "Technical Program Manager, AI Delivery" at Cohere: $140K, $220K • Offers Equity • Multiple Ranges
• "AI Solutions Strategist" at Ramp: $170K, $230K • Offers Equity
• "Global Public Policy Manager, Compute, Infrastructure & Sovereign AI" at Cohere: $150K, $240K • Offers Equity • Multiple Ranges

Notice the Cohere posting is quoted in Canadian dollars, so if you are applying from the US to a role posted with a non-USD figure, ask which currency the offer will actually be denominated in before you compare it to anything else. "Multiple Ranges" on the Cohere listings usually means the band shifts with level or location, so ask which band applies to you. Ask early.

Typical reported ranges by seniority#

These are not from the listings above and they are not guarantees. They are rough reported ranges for US data and AI roles in 2026, and they vary a lot by city, company stage, and how scarce your specific skill is, so verify against current postings and official sources before you plan around any of them. Treat them as a starting point only.

For a data analyst or junior data scientist, total cash often lands somewhere in the $85,000 to $130,000 range per year. Mid-level data scientists and machine learning engineers commonly report $140,000 to $220,000 per year in base. Senior and staff level engineers in AI often report $220,000 to $350,000 per year in base before equity.

Leadership roles like the Senior Director title above can go higher, but the bands are wide and often include a large equity component. Wide bands, big variance.

San Francisco and New York pay at the top of these ranges. Denver and remote roles often sit lower, sometimes meaningfully so, and a remote offer from a Bay Area company may be adjusted to your location, so ask early rather than assuming the top of the band. Don't assume.

How US pay packages are built#

US tech offers are usually described in terms of base salary, annual bonus, and equity. Base is the number you can count on. Bonus is often a target percentage of base and is not guaranteed, so ask what percentage of target the company has actually paid in recent years. Get the history.

Equity is the biggest source of confusion. It can be RSUs at a public company, or options or restricted stock at a private one, so ask for the number of shares or units, the grant schedule, the vesting cliff, and what happens to unvested equity if you leave or are laid off. Do not treat a private company equity number as cash. It is not.

You will not see a 13th month salary or superannuation in US offers. Those are norms in other countries. In the US, look instead for a 401(k) match, signing bonus, relocation, health insurance premiums, and paid time off, all of which move the real value of the offer up or down.

How to negotiate#

Do not give a number first if you can avoid it. If asked for your expectations, turn the question back: "What is the approved range for this role?" Then anchor in the band, not in your last salary.

Worked example, a full script for the moment you get a verbal offer at $180,000 base per year:

"Thank you, I am excited about the role. Before I accept, I want to talk about the package. Based on the scope we discussed and what I am seeing for similar senior machine learning roles, I was expecting base closer to $210,000. Is there flexibility on base, or on the equity grant, to close that gap?"

If they say base is fixed, ask about equity, signing bonus, and the review timeline instead, because those levers often move even when the salary band does not and a recruiter can usually work them without another approval cycle. Get the final numbers in writing before you resign anywhere. Non-negotiable.

• Ask for the full written offer before you respond to any verbal number
• Get the base, bonus target, equity type and amount, and vesting schedule in writing
• Ask how the band for your level is set and where you sit in it
• Ask what percentage of the bonus target was paid out in the last two years
• Ask about the 401(k) match, health insurance premiums, and PTO policy
• Ask what happens to unvested equity if the company is acquired or you are laid off
• Confirm your work location, remote status, and any pay adjustment tied to it
• Give yourself at least a few days before accepting

If you want a second set of eyes on the numbers before you counter, our free salary negotiation guide walks you through the ask step by step.

Where to look next#

For context on the wider US market beyond data and AI, the JobRise Hiring Report: the US shows 13,720 open listings across all categories in the United States, with 8,471 posted in the last 30 days. Data and AI is a slice of that, not the whole thing, so keep your search broader than one category if you are early in your search. Stay broad.

FAQ#

How many data and AI jobs are open in the US right now?

As of 14 October 2026, the JobRise feed shows 519 open Data & AI listings in the United States, with 305 posted in the last 30 days. These are listing counts from our feed, not a market survey, so treat them as a snapshot.

Why do so few listings show salary?

Only 15 of 519 listings in this snapshot show pay. Many US employers keep the number private until late in the process, which is exactly why you should ask for the approved range early and anchor your ask in that band.

What is a realistic salary for a mid-level data scientist in the US?

Reported ranges for mid-level data scientists and machine learning engineers in 2026 commonly sit around $140,000 to $220,000 per year in base, before equity. These vary by city and company, so check current postings and official sources before you plan around them.

Should I include equity in my salary expectations?

Yes, but count it separately from base. Base pays your bills, equity is a bet on the company, and the value of RSUs or options can move a lot between the offer date and when they actually vest.

Is it worth negotiating if the offer already seems fair?

Yes, and it rarely hurts to ask once, politely and with a reason. Ask for the range, name the scope you discussed, and give a specific number or a short list of asks like base, signing bonus, or equity.