What AI/ML Engineering roles do you place?

In AI/ML Engineering we place: Machine learning engineers, mid to staff level; Senior and lead AI/ML engineers; AI Leads and Heads of Machine Learning; ML platform and infrastructure engineers; Engineers moving from research into production work.

How quickly can you deliver a shortlist?

Brief to shortlist is approx. one week. Brief to offer is approx. three weeks for a permanent (full-time) hire and approx. one week for a contract hire.

Do you work retained or contingent?

Both, and they buy different things. Retained buys a full market map, direct approaches to people who are not looking, and sustained work on your brief alone, with the fee split in thirds: a third on engagement, a third on shortlist, a third on placement. Contingent works the reachable market quickly, with no fee until you hire someone. Payable when the candidate signs their employment contract, or on their start date. Retained suits scarce and senior hires; contingent suits most permanent (full-time) roles.

What we place

AI and Machine Learning Engineering Recruitment

Digitalent, the AI/ML executive search firm, places machine learning engineers and AI engineering leaders into VC-backed startups and established companies across the US, UK and Australia. These are the people who take a model from something that works in a notebook to something that runs in production and keeps running.

Get in touch Recent placements

AI/ML Engineering at Digitalent

500+

AI/ML placements since January 2021

200+

executive and leadership hires

90%+

still in post after 12 months

By market: 300+ US, 100+ UK, 100+ Australia
By seniority: 200+ executive and leadership hires, 300+ senior individual contributors
Brief to shortlist: approx. one week
Brief to offer: approx. three weeks for a permanent (full-time) hire, approx. one week for a contract hire

Figures updated August 2026.

Roles

What we place in AI/ML Engineering

These roles come up most often, at both leadership and senior individual contributor level.

Machine learning engineers, mid to staff level
Senior and lead AI/ML engineers
AI Leads and Heads of Machine Learning
ML platform and infrastructure engineers
Engineers moving from research into production work

The hard part

What makes these searches hard

The most common failure is hiring a researcher for a production problem. Someone excellent at moving a benchmark can be the wrong person entirely for a team that needs a model shipped this quarter, and most interview processes reward the wrong one of the two.

We establish at kickoff whether the first hire needs to ship or needs to investigate, because a company at Series A usually needs the former and often interviews for the latter. Where a role genuinely needs both, that changes the pool and the timeline, and we will say so rather than take the brief as written.

Case study

Placements in this area

A hands-on AI Lead to start a team from nothing at PE Limited (Petex)

PE Limited, also known as Petex, a software company in Guildford, appointed its first AI Lead through Digitalent: a hands-on engineer senior enough to set the direction and build the first systems personally.

PE works five days a week from its Guildford office, so the shortlist had to be people already within reach of it or genuinely willing to move, which rules out most of a market that has got used to working from anywhere. The brief also needed a builder rather than a manager: there was no AI function to inherit, so the first hire had to be senior enough to set the direction and hands-on enough to write the first production code themselves. And because the person who would go on to lead the team had not been appointed yet, the CEO was the hiring manager throughout, which is a different conversation from the one you have with an engineering leader.

PE's first AI team was built around this appointment, every hire through us. The company went from no in-house AI capability to shipping AI products on top of its existing software suite, which landed well with customers across the oil and gas industry.

Client: PE Limited, also known as Petex
Stage: Privately held, established
Location: Guildford, Surrey
Roles: AI Lead
Brief to shortlist: two weeks
Brief to offer: four weeks

The first AI/ML engineer into a brand new team at PE Limited (Petex)

With the AI Lead appointed, PE Limited needed the engineer who would build alongside them, and for a while the only other AI/ML engineer in the business.

Outside the AI Lead this was the only AI/ML engineer in the business, so there was nobody to learn the ropes from and nowhere for a weak hire to hide. Technical depth was the entry requirement rather than the thing that decided it. What decided it was whether someone could hold their own with the wider engineering team and with the business, inside an established company where the AI function was brand new and still had to earn its place. Guildford, five days a week in the office, narrowed the pool again.

Responsibilities have grown steadily since. They lead on projects, help with hiring into the team, and have built up real domain knowledge in oil and gas, which in a business like this one is most of what separates a capable engineer from a genuinely useful one.

Client: PE Limited, also known as Petex
Stage: Privately held, established
Location: Guildford, Surrey
Roles: Senior AI/ML Engineer
Brief to shortlist: two weeks
Brief to offer: four weeks

An AI Engineer to build against the architecture at FleetGuru.ai

Five months later, FleetGuru.ai came back to Digitalent for the engineer who would build against that architecture, a Python-native AI engineer able to work across a C#/.NET codebase.

With the architecture set, the second search needed a builder, and the stack made that narrow. The core systems are C#/.NET with React and GraphQL, so a Python-native AI engineer had to be able to bridge that, work with the existing code and design APIs the engineering team could actually consume. The bar was set by a Chief AI Product Officer who reads twenty research papers a month and did not want data scientists. He wanted software builders who understood evaluation, latency, state machines and the Model Context Protocol.

Shortlisted in under a week. The six weeks to offer were mostly a month of pre-booked holiday, after which the candidate cleared a rigorous technical panel and was offered within a week, above the level the role had been briefed at. They own the time-series predictive maintenance modelling. Both hires work from outside the Gold Coast on hybrid arrangements agreed with the founders before the searches opened, which is what made either of them possible.

Client: FleetGuru.ai
Stage: B2B software scale-up, spun out of an established marketplace
Location: Gold Coast, Queensland
Roles: AI Engineer
Brief to shortlist: under one week
Brief to offer: six weeks

Also

The other areas we cover

We work across 7 areas of AI and machine learning. Most clients hire across more than one.

FAQ

AI/ML Engineering: frequently asked questions

What AI/ML Engineering roles do you place?: In AI/ML Engineering we place: Machine learning engineers, mid to staff level; Senior and lead AI/ML engineers; AI Leads and Heads of Machine Learning; ML platform and infrastructure engineers; Engineers moving from research into production work.
How quickly can you deliver a shortlist?: Brief to shortlist is approx. one week. Brief to offer is approx. three weeks for a permanent (full-time) hire and approx. one week for a contract hire.
Do you work retained or contingent?: Both, and they buy different things. Retained buys a full market map, direct approaches to people who are not looking, and sustained work on your brief alone, with the fee split in thirds: a third on engagement, a third on shortlist, a third on placement. Contingent works the reachable market quickly, with no fee until you hire someone. Payable when the candidate signs their employment contract, or on their start date. Retained suits scarce and senior hires; contingent suits most permanent (full-time) roles.

Hiring in AI/ML Engineering?

Tell us the role and the market. A short call is usually enough to tell you what the pool looks like for it.

Get in touch

Hiring in the US, the UK or Australia? You will reach the person who runs searches in that market.