AI/ML Jobs Hiring Report — June 2026

Key facts

• As of June 2026 there are 9,228 open AI/ML roles across 2,855 companies on the direct-apply boards I track.
• Hiring is still expanding: +1,571 net new roles over the last 28 days (3,936 opened against 2,365 closed).
• 519 companies posted their first AI/ML role in the past 28 days — fresh demand, not just churn at the usual names.
• Only 17% of postings disclose pay; among those that do, the median band is $154,391–$214,969/yr (n=279).
• Machine Learning Engineer is the best-paid common title at a $174,000–$253,500 median band (n=56).

The number that jumped out at me this month: 519 companies opened their first AI/ML posting in the last 28 days. That's not the established labs adding headcount — it's new entrants showing up on the board for the first time. When more than five hundred companies decide in a single month that they need someone to build this stuff, the demand is broadening, not concentrating. That matters more for your odds than any single hot startup's hiring spree.

How many AI/ML jobs are open right now?

There are 9,228 open AI/ML roles across 2,855 companies as of June 2026. That works out to roughly 3.2 roles per hiring company — so this isn't a handful of giants carrying the whole market. The spread across nearly three thousand employers is the healthier signal here, because it means your search isn't hostage to whether two or three big names happen to be in a hiring window.

You can see the live list on the AI/ML jobs board, but the aggregate is what tells the story: a wide base of employers, most of them hiring in modest numbers.

Is AI/ML hiring growing or cooling?

Growing, clearly. The last 28 days produced 1,571 more openings than closings — 3,936 roles opened against 2,365 closed. That's a net expansion of around 17% on the standing total in a single month, which is a brisk pace by any measure.

I want to be honest about what the closed number means, though. 2,365 roles came off the boards in the same window. Some of those were filled, some were quietly pulled, and from the outside I can't always tell which. So the +1,571 is best read as net demand on the open market, not a count of hires. Even with that caveat, opens outrunning closes by a margin this size doesn't happen in a contracting market. If you've been sitting on the fence waiting for conditions to improve, the conditions are already here.

Which roles and seniority levels dominate?

Engineering swamps everything else: 8,823 of the 9,228 roles — about 96% — are engineering positions. Data analytics is a distant second at 312, and after that the numbers fall off a cliff (operations 6, design 5, sales 4, product 4). If your background is in AI/ML product management or analytics, the openings exist, but you're fishing in a much smaller pond and should calibrate expectations accordingly.

On seniority, the largest single bucket is "unspecified" at 4,524 — nearly half the postings don't pin down a level in a way I can parse cleanly. Of the roles that do declare a level, the shape is decidedly senior-heavy:

• Senior: 2,729
• Principal: 1,203
• Director: 385
• Junior: 279
• Executive: 85
• VP: 23

Principal roles outnumbering junior ones by more than four to one is the detail worth sitting with. Companies are hiring people who can build production systems with little hand-holding, not training up beginners. For experienced engineers that's a strong market. For early-career candidates, 279 explicit junior openings against 9,228 total is a thin slice — though I'd note that a chunk of that giant "unspecified" pile almost certainly includes roles open to less experienced people who just didn't tag the level. Don't write off a posting because it skips the seniority label.

What do AI/ML jobs pay?

Among the 17% of postings that disclose pay, the median band lands at $154,391–$214,969/yr (n=279). The blunt caveat first: that's a minority of postings, weighted toward employers in jurisdictions that require pay transparency, so treat it as a directional read rather than gospel for the whole market. The full breakdown lives on the AI/ML salaries page.

Drilling into specific titles sharpens the picture:

• Machine Learning Engineer: $174,000–$253,500 (n=56)
• Research Scientist: $150,000–$237,900 (n=25)
• AI Engineer: $150,000–$205,000 (n=27)

The ML Engineer band is the one to anchor on — it's both the best-paid and the best-sampled of the three, with a top end past a quarter million. The "AI Engineer" title pays a notably tighter and lower range than "Machine Learning Engineer," which tracks with what I'd expect: AI Engineer increasingly means applied integration work, while ML Engineer still carries the heavier modeling and systems weight. The sample sizes here are small (56, 25, 27), so don't anchor a negotiation on a single band, but the ordering between them is consistent enough to act on.

Which companies are hiring AI/ML fastest?

BJAK leads net new hiring over the last 28 days at +21, with 22 active roles — meaning almost all of its current AI/ML openings appeared this month. You can see its postings on the BJAK board. The rest of the top of the table:

• BJAK — +21 net, 22 active (careers)
• LILT — +17 net, 92 active (careers)
• Mistral AI — +16 net, 70 active (careers)
• Sarvam — +14 net, 14 active (careers)
• Gen Digital — +14 net, 14 active
• Nscale — +13 net, 13 active
• Coupang — +12 net, 30 active
• Life360 — +12 net, 26 active
• Roku — +12 net, 24 active
• Drata — +11 net, 15 active

Two patterns stand out. First, LILT and Mistral AI are the only two on this list carrying large standing footprints — 92 and 70 active roles respectively — while still posting double-digit net growth. That's the profile of a company scaling on top of an already-substantial team, which usually means structured hiring and defined ladders rather than a scramble.

Second, look at how many of these are at or near "everything's brand new": Sarvam (14 of 14), Gen Digital (14 of 14), Nscale (13 of 13). When active roles equal net new roles, the company effectively didn't have an AI/ML presence on the board a month ago. Those are the spots where the team is being shaped right now — higher ambiguity, but also more room to define what your role becomes. The full ranked list updates on the AI/ML movers page if you want to watch how it shifts week to week.

It's also worth registering the range of company types here. A foundation-model lab (Mistral), a translation platform (LILT), a consumer-safety app (Life360), a streaming company (Roku), a security firm (Drata), an e-commerce giant (Coupang). AI/ML hiring isn't confined to the labs everyone reads about — it's embedded across product categories, and that's exactly where a lot of the quieter, less-contested openings sit.

Where are the remote AI/ML jobs?

Remote roles make up 32% of current openings — roughly one in three. That's a meaningful share but the clear minority, so if remote is non-negotiable for you, plan on filtering hard and competing for a smaller pool. The flip side: two-thirds of roles carry a location expectation, which is worth knowing before you assume the market is remote-by-default. It isn't.

Is this a good time to look for an AI/ML job?

Yes, with conditions. The headline math is favorable — 9,228 open roles, net expansion of +1,571 in 28 days, and 519 first-time hirers entering the market. Demand is broad and growing. But the same data tells you who that demand is for: senior and principal engineers, overwhelmingly in engineering functions, frequently on-site.

If you're a mid-to-senior engineer with shipping experience, this is about as good a window as I've measured. The volume is there, the pay band tops out well past $250k for ML Engineers who disclose, and you have real leverage to be selective between an established scaler like LILT and a from-scratch team like Sarvam or Nscale.

If you're early-career, the path is narrower but not closed. Explicit junior postings are thin at 279, so your best move is to mine the 4,524 "unspecified" roles — many of those don't gate on years of experience, they just didn't bother to tag a level. Read the requirements, not the title.

The takeaway for job seekers

Aim where the data points. Target engineering roles — that's 96% of the market — and lead with concrete production experience, because principal openings outnumber junior ones four to one and employers are buying people who can build without supervision. If pay matters, position toward "Machine Learning Engineer" rather than "AI Engineer" where your skills allow; the band runs higher and samples deeper. Apply to a mix of the big scalers and the fresh teams from the movers list — the established names offer structure, the brand-new boards offer the chance to define a role before anyone else fills it. And since only 17% of postings show pay, walk into every conversation already armed with the disclosed bands from the salaries page, because most employers won't volunteer a number and you'll want one ready.