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Senior ML Platform Engineer
Series C fintech, London
Moved model serving from overnight batches to real time for a payments product used across Europe.
1 month notice
Confirmed open, October
The hiring platform for AI companies, with humans behind it
A few years ago, no one was recruiting
Hire for AI roles with evidence, not CVs. The platform finds the people who can already do the job, interviews every one of them, and sends you a shortlist you can read in five minutes. A person vets everyone before they reach you.
Tracked continuously
01Funding rounds02Office and headcount expansion03Leadership changes04Hiring surges05New job families06Technology shifts
What the platform covers
Hiring for AI companies. Every function, every level.
Most hiring help in AI stops at research and engineering. But an AI company also needs its first finance lead, its general counsel and its first salesperson, and those hires are just as hard to get right. The platform covers all of them.
Research and engineering
Models, infrastructure, evaluation, applied research, security
Product and design
Product management, design, forward deployed engineering
Go to market
First commercial hire through VP, marketing, partnerships, customer
Corporate
Finance, legal, people, operations, data protection
Leadership
Founding team, functional heads, executive and board hires
From the first hire in a discipline to replacing the person who built it.
New AI job families
Often the brief is for a job that has no benchmark.
Whole disciplines have appeared to build, audit and pay for AI, and job architecture has not caught up. There is no salary band to work from, no comparable hire to point at, and a keyword search returns nothing, because the people already doing the work do not use the title yet.
Build it
Prove it
Run it
Agentic Workflow Architect
Designs multi-agent ecosystems where autonomous systems carry complex, multi-step business logic without a person in the loop, and makes sure that logic does not drift or fall into recursive loops.
Why it is hard to fill
Almost nobody has run agents in production for longer than a year. Filter on years of experience and you remove every candidate who can actually do it.
Prompt and Context Architect
Structures multi-turn context windows, persona frameworks and tool-calling parameters so output stays consistent across complex enterprise workflows.
Why it is hard to fill
The title is contaminated. Advertise it and you get thousands of applicants with no engineering background, which makes inbound the worst signal in the market.
Synthetic Data Curator
Generates, cleans and tags high fidelity artificial datasets to train advanced models, now that clean real-world training data is depleted or restricted by privacy law.
Why it is hard to fill
Hardly anyone uses the title on their CV. The people doing the work call themselves data scientists or research engineers, so a keyword search returns nothing.
Algorithm Auditor and Bias Analyst
Runs technical evaluations on deployed models to catch hallucination, security exposure and systemic demographic bias, before the system meets a regulator.
Why it is hard to fill
Has to be credible to a regulator and to an engineering team at once. Audit backgrounds cannot read the model. Machine learning backgrounds cannot write to a regulator.
AI Governance Lead
Keeps enterprise deployments inside evolving global frameworks such as the EU AI Act, maintaining model risk documentation and liability protocols.
Why it is hard to fill
The EU AI Act pushed the whole market at the same small pool simultaneously. Most available candidates have policy depth and no deployment experience.
AI Documentation and Verification Specialist
Verifies, grounds and fact-checks what AI scribes and copilots produce in high-stakes fields such as medicine, law and corporate compliance.
Why it is hard to fill
Needs a real professional qualification plus a willingness to work next to a model. That combination almost never answers an advert.
AI FinOps Specialist
Manages token budgets, model right-sizing and GPU compute cost, so an enterprise AI operation does not burn capital faster than it generates revenue.
Why it is hard to fill
It sits between finance and infrastructure, so one function advertises it and the other understands it. Cloud FinOps people miss the model economics. Machine learning people miss the profit and loss.
Robot Relationship Manager
Oversees the physical and behavioural interaction between human staff and collaborative robots across manufacturing, logistics and clinical environments.
Why it is hard to fill
It belongs to operations, not technology, so it never reaches a technical recruiter. The people who can do it come from safety, industrial engineering or clinical operations.
See all the new AI roles →
Every new job family we track: what each role does, what a strong hire delivers, and where the people doing it work today.
How the platform works
Everyone has the same database. Almost nobody is watching.
Anyone can buy an index of eight hundred million profiles, and most recruiters now have. It tells you who exists. It does not tell you which company is about to need them, or which person has just become reachable. A vacancy is the last event in a long sequence, so we work from the earlier ones.
Always on
It is watching before you brief it
Job boards, professional networks, company sources and news, read continuously rather than started the day a mandate is signed.
Scored
Signals are weighed before anyone acts
Funding, expansion, leadership change, hiring surge, new job family, technology shift. A weak signal never becomes a phone call.
Read
We read whole careers, not job titles
Every role a person has held, from every source the platform can find, read against what your role needs. It is how you meet the person whose title does not exist yet.
Personal
Every approach is written to one person, about one role
Not a template with a name dropped in. It says why them, which is why people answer. Anyone who says not now hears nothing more.
People
One person stays with your hire
Software carries the volume, so the person you brief stays with you from the first call to the offer. Nobody hands you on.
Confidential
Your name is not in the approach
Candidates hear about an anonymised business and the work itself, checked before anything sends, until you are both ready.
Talent market mapping
See who's out there before you sign anything.
Tell us the role and the platform maps the market for it, free. A sample of people who could do the job and are open to a move, reduced to what matters: how senior, the kind of company, what they've built. No names, so you judge the work and not the logo.
01
Judge the work
No employers, no schools, no trail back to a profile.
02
Talk to people who'll talk
Every card says when that person last told us they were open.
03
One click to a name
Point at someone and we arrange the introduction, with their agreement.
Anonymous profile
Head of AI Governance
Listed AI company, remote UK
Built the model risk framework two product lines shipped under, and took it through regulator review.
3 months notice
Confirmed open, October
Anonymous profile
Founding Account Executive
Seed developer tools, New York
Closed the first enterprise contracts for a developer tool with no sales team behind them.
2 weeks notice
Spoke to us in September
Anonymous profile
Finance Lead, first hire
Series A AI infrastructure, London
Ran the close, the board pack and the first fundraise model at a company of forty people.
1 month notice
Confirmed open, October
Interviews with Nathan
Every name on your shortlist has already been interviewed.
Nathan interviews every candidate for your role, live on video, before you give anyone an hour. He has read their whole career before he says hello, so the questions are about their work, not a script. A person reviews every interview before a name reaches you.
Nathan
Speaking
Speaking
You said the first fix was ownership. Who pushed back on that, and how did you bring them round?
Transcript LIVE
NathanYour CV says you moved model serving to real time. What broke first, and how did you find out?
CandidateA cache nobody owned. We lost a day to it. After that every service had a named owner and an alert before it had a dashboard.
Nathan, following upYou said the first fix was ownership. Who pushed back on that, and how did you bring them round?
What you read afterwards
Running models in productionMust-have
“Every service had a named owner and an alert before it had a dashboard.”
Working with research teamsGood
Reviewed by a person on our team
- 01Your briefWhat the role needs, in your words.
- 02The planFrom your job description, your company and the market, checked by a person.
- 03Nathan interviewsLive, on video, whenever suits the candidate.
- 04A person reviewsEvery interview, before anything is shared.
- 05Your shortlistTheir answers, in their own words.
Built from the role, their career and the market
Questions come from your job description, from what this person has actually done, and from what is happening in your market right now. Rehearsed answers don't get far.
He follows up
A thin answer gets a second question, and a third if it needs one. The way a good interviewer would, every single time.
Set in a company like yours
Scenarios happen in a business your size, at your stage, using your tools. Your name never comes up.
No two interviews alike
Each candidate gets the topics in a different order and questions of their own, so nobody can pass the answers round.
Their words, not a number
You read what they said against each thing the role needs, quoted. You see why someone is on the list, and where they were thin.
A person makes the call
Nathan does the sift. A person on our team reviews every interview and speaks to anyone before you meet them.
For candidates
About 25 minutes, whenever suits you, on your phone or laptop. Questions about your own work, and a fair hearing for it.
Shortlist GTC-S4KQP22 of 5
AI Governance Lead
- Based
- London, hybrid
- Why her
- Took two model families through EU AI Act sign-off
- Moving
- Open, not searching
In her interview“The hard part wasn't the paperwork. It was getting engineers to treat a risk review as part of shipping.”
Download PDF
Your shortlist
A link and a six digit code.
No account, no login, nothing to learn. It lands in your inbox or Slack and opens on your phone between meetings.
- One page per person: why them, and how the interview went in their own words.
- Clips they agreed to share, and a PDF for whoever else gets a say.
- Worth meeting, and we make the introduction. Pass, and the next names reflect it.
Talent benches
Your second hire should start warmer than your first.
For roles you fill again and again, the platform keeps a named bench of people for you and keeps the conversation going. When the next role opens, you start with people who already know about you, not a blank page.
- See how many confirmed this month that they're open, and who has gone quiet.
- Pull someone into a live role and the bench stays for the next one.
Platform engineering, London40 people
18Confirmed open this month
9Spoke to us in the last 60 days
13Not heard from lately
Staff Engineer, Series B data platformReplied 3 days ago
Engineering Manager, public cloud companyReplied last week
Senior SRE, Series C insurtechEmailed 12 Sep
Principal Engineer, seed AI toolingLast spoke in July
For candidates: Lexa, AI interview and career coach
Walk into the interview having already sat it.
Lexa is a coach on video, any time you need one. She prepares you for a real interview at a real company, or works through your next career move with you, and she remembers where you left off.
Mock interviews for the job you want
Pick one of our roles or paste any job advert. Lexa reads it with your CV, looks up the company that's hiring, and asks what their interviewer would ask.
Practice mode
Coaching after every answer: what worked, what to tighten, a stronger way to say it. Then another go.
Real mode
Straight through, follow-ups and all, like the day itself. Then an honest debrief.
- Questions built from the role, the company and your own experience
- Why this company, why now: she makes sure you can answer it
- A written report with a stronger version of each answer
Career coaching, face to face
A real conversation about where you are and where you want to go. She asks good questions, challenges you when something doesn't add up, and gives you wording you can use the same day.
- CV and positioning
- LinkedIn and personal brand
- Your next move
- Salary and negotiating offers
- Confidence and nerves
- Changing direction
- A sharper LinkedIn headline and CV lines, written with you
- How to phrase the pay conversation before it happens
- You leave with a plan you agreed together, in writing
Mock interview: Head of Product, Series B AI companyYour report
Why this company, and why now?
What you saidI've always been interested in AI and I really like what you're building. It feels like the right time for me to move.
A stronger versionYou've just gone from pilots to paying customers. I've made that move once, at a company your size, and I know where it breaks: the second customer, not the first. That's the part I want to own.
- Lead with the result, then how you got there.
- Name one trade-off you made and why.
- Keep technical answers under two minutes.
She remembers you
Lexa keeps a short note of your goals and reads it before every session, so each one picks up where the last left off.
Going for Head of Product roles at AI companies. Strong stories, tends to bury the result. Next: pricing questions and the salary conversation.
Yours, and private
Recording, transcript and report for every session, kept in your account. Never shown to employers, and it has no bearing on any role you apply for.
About 20 minutes on videoAny role, any companyPhone or laptopPractise for this role, on every role on the platform
Apply in a tap
Then see where every application stands, from your own account.We write to you, not at you
When we get in touch it's about one role and why you. Say not now and we stop.Your data, your call
Download it, withdraw consent or delete your account yourself, whenever you like.People decide
Software does the legwork. People make the calls.
We say where AI is used
In plain words, before it's used, to hiring teams and candidates alike.
A person reviews every interview
Nobody is turned down by software alone.
Ask for a second look
Any candidate can ask a person to review a decision, from their account.
Clips only with consent
Interview clips reach a client only when the candidate has agreed.
Questions
What people ask us first.
Anything else, start hiring and we will come back to you.
What does Generative Talent Co do?
Generative Talent Co is a hiring platform for AI companies, with humans behind it. It finds, interviews and shortlists people across research and engineering, product, go to market, corporate functions and leadership, from the first hire in a discipline to the founding team, including confidential searches.
How does Nathan, the AI interviewer, work?
Nathan runs a live video interview built from your job description, each candidate's own career and the market, follows up when an answer is thin, and gives every candidate the topics in a different order. A person reviews every interview before anyone is shared with you.
Does AI decide who gets hired?
No. Software does the searching and the first interview. A person at Generative Talent Co reviews every interview and makes every decision, and you make the hire.
Can a hire stay confidential?
Yes. Candidates hear about an anonymised business and the work until you are both ready, and your name is never in the approach.
What is a free market map?
A free, anonymised sample of the people who could do your role and are open to a move: how senior, the kind of company and what they have done, with a picture of the market for it. The full view comes in the bench we build for you.
What is Lexa, and does practising affect my application?
Lexa is our interview and career coach for candidates, on video. Sessions are private, never shown to employers, and have no bearing on any role you apply for.
How do your fees work?
No fee unless you hire. We talk you through terms when we speak.
Meet Beth
Tell Beth who you're hiring. She'll show you how we'd approach it.
Beth knows how we work inside out and keeps up with your market as it moves: who's raising, who's hiring, what roles pay. Ask her anything, right here. In a few minutes you'll know how we'd go about your hire, and you'll have a time in the diary to start it.
BethGenerative Talent Co
Online
Hi, I'm Beth. Hiring at an AI-native company, or using AI to scale? Tell me the role and I'll show you how we'd approach it with our proprietary search technology.
Talking uses your microphone.
On a call with BethConnecting
- Answers day or night
- Knows your market this week
- No fee unless you hire
- Books your call there and then
- Type or talk, your choice
Start hiring
Tell us who you need.
About two minutes. We come back with whether this is a hire we can help with and how we would go about it. If it is not one for us, we will say so.