For employers
An AI-only job board, read at source from the companies building AI. Feature a role that stands out from the list, or give candidates the full story of your team on your own employer page.
Proof, not promises - measured on our own board
Every Apply click lands directly on the employer's own careers site - we send candidates, not resumes.
Post a role
$299
Meridian AINew York, United StatesFull-time
Ship the retrieval layer that grounds our assistant in customer documents, and measure every change against a real eval set.
Fictional example listing.
Stand out
Custom - request pricing
AI at Meridian AI
The job post
A paid post is not another row in the list. It carries your logo large, a one-line pitch, your key skills and a clear Sponsored label, while salary and posted date stay in the same columns as every other job so it is easy to compare. Meridian AI is a fictional example company; these are not real listings.
Meridian AIUnited StatesFull-time
Own the serving stack behind a 70B model: batching, KV-cache and kernel work that turns p95 latency into a number customers notice.
Harbor AnalyticsSan Francisco, United States
Kestrel SystemsGermany
Base $299 listing, no add-ons
Meridian AIUnited StatesFull-time
Own the serving stack behind a 70B model: batching, KV-cache and kernel work that turns p95 latency into a number customers notice.
Your brand
Your logo, shown large and handled properly on light or dark artwork. Add your own brand color to the card for +$59.
Your role, told properly
Title, salary, location, work model and employment type, plus a one-line hook from your description and your key skills.
Featured and clearly labelled
The Featured design on the main board, in search, on the title and country pages that match your job, and on the remote pages when the role is remote. Pin it to the top of the board from +$99.
Direct applicants
Apply goes straight to your ATS, careers page or inbox. No middleman, no resume database, no candidate gating.
Meridian AIUnited StatesFull-time
Own the serving stack behind a 70B model: batching, KV-cache and kernel work that turns p95 latency into a number customers notice.
Employer-brand page · full example
Your company already has a careers page. We turn it into the page AI candidates actually explore before they apply: the work, the team, compute, the interview and your open roles, on the board they already use.
We make large models fast, cheap and honest to evaluate.
Illustrative photo. Meridian AI is a fictional company created to show the full version of an employer-brand page.
Meridian AIUnited StatesFull-time
Own the serving stack behind a 70B model: batching, KV-cache and kernel work that turns p95 latency into a number customers notice.
Meridian AILondon, United KingdomFull-time
Build the eval suites that decide whether a model ships. You will design the benchmarks, not just run them.
Meridian AINew York, United StatesFull-time
Ship the retrieval layer that grounds our assistant in customer documents, and measure every change against a real eval set.
On a real employer page your current openings appear here automatically, from the roles we already read from your applicant tracking system.
Meridian AI is a 60-person applied-AI lab split between New York and London. It builds inference and evaluation tooling for teams that want to run capable models without a frontier-lab budget. Small teams own a system end to end: the people who write the serving code carry its pager, and research engineers ship their own evals to production.
What it's like to work on AI here
Research engineers do not hand results over a wall. Whoever proposes a model change pairs with the serving team until it is live, and the change is only "done" when it is running behind production traffic with its eval attached.
“At my last lab a good result meant a slide. Here it means I am on the rollout channel the week it ships.”
Every researcher has a standing GPU quota that does not need a ticket, and larger training runs go through a weekly allocation meeting with published criteria, so nobody wins compute by lobbying the loudest.
No model change ships on "it feels better". Each one carries the eval that justified it, the eval suite has its own owners, and a result that does not reproduce on a second run is treated as a finding, not an embarrassment.
“I have killed my own launch twice because the eval did not hold up. Nobody argued with the number.”
The core problem is serving a 70B-parameter model at a p95 latency customers notice, on hardware they can afford. That means batching, KV-cache and kernel work where a 5% win is worth a quarter of effort.
On-call rotates one week in six, is paid as a flat stipend on top of salary however many pages come in, and median pages per shift is tracked openly. It has stayed under 3 for four quarters after a push to delete noisy alerts.
Everyone gets a $5,000 annual budget for compute, conferences or courses, self-service up to the cap. Each quarter the whole lab takes a four-day research sprint to work on anything that might matter next year.
A six-month mentorship pairing runs twice a year for anyone moving tracks, and moving from ML engineering into research engineering is a normal path here, not an exception.
“I joined to run training infrastructure. Eighteen months later I was designing the evals.”
Before a model is offered to customers it goes through an internal red-team week run by people who did not build it, and the release notes say what was tested and what was not.
Hybrid in New York and London, or remote within US and UK time zones, with a four-hour overlap for synchronous work. The whole lab meets in person twice a year.
Intro call
Your background, what you want next, and logistics.
30 min · The hiring manager
Take-home or pairing
Your choice: a short take-home or a pairing session on a real (simplified) problem from the team.
Up to 3 hours · An engineer from the team
Team loop
A system-design session and a walkthrough of a paper or code you know well.
2 hours · Two engineers and a research engineer
Decision
You hear back with a decision and the reasons.
Within 5 working days
What to expect
What we evaluate
Come ready to discuss
Faster, cheaper inference with evaluation you can trust puts capable models within reach of teams that could never afford them.
Ana P.
“A good result here means I am on the rollout channel the week it ships.”
Tom W.
“I have killed my own launch twice because the eval did not hold up.”
Raj M.
“A 5% latency win is a real project here, not a side quest.”
Sam O.
“I joined to run training infrastructure and now design the evals.”
Lena H.
“If a number does not reproduce, we say so in the release notes.”
Built around what AI candidates want to know
What does the team actually work on?
Does research reach production here?
How much compute will I get?
How is the team organised, and who does it report to?
Remote, hybrid, on-call, hours?
What is the interview like, and how do I prepare?
Who would I work with?
What roles are open right now?
Your employer page
We build it with you, from your own content
Nothing goes live until you have checked it. Tell us who you are and we will send pricing and next steps.
A specialist board
Only AI roles - no generalist noise. Candidates come here to research roles, pay and companies.
Read at source
Every role comes from the employer’s own applicant tracking system, so listings reflect what is genuinely open.
Classified, not dumped
Worldwide coverage, filed into nine AI functions rather than one undifferentiated feed.
Get started
We build it with you from your own content, and nothing goes live until you have checked it. Tell us who you are and we'll send pricing and next steps.