Whether you’ve landed a mega funding round or that-labour-of-love tech project has finally reached commercial maturity, scaling your recruitment process comes with the territory for any successful AI company.

When your hiring conversations go from ‘we need a couple of excellent engineers’ to ‘we need another fifty people in the next twelve months,’ you’re looking at a challenge in organisational design.

It’s staffing adjacent (because you’ll always need to recruit great people), but fifty good hires made in the wrong order will still leave you with the wrong team. 

Building a workforce at scale means looking at what each round of hiring is supposed to change for the business without treating headcount like the outcome. 

What does this look like for your business? 

It depends on what you’re trying to get done, especially when you’re hiring AI talent, where one title can mean 400 different things. You’ll need to limit this variation by getting specific with your objectives. 

Whenever DeepRec.ai supports on high-volume hiring projects, whether that’s an embedded hiring solution or a defined project team, we typically account for: 

  • Training infrastructure: distributed systems engineers who understand GPU clusters, orchestration, parallelism and frameworks such as PyTorch, DeepSpeed or Ray.

  • Inference and serving: engineers focused on latency, throughput, quantisation, model serving and getting expensive models into production without the economics falling apart.

  • Research and applied ML: people who can move from experimentation into working products, whether that means multimodal models, reinforcement learning, computer vision, NLP or domain-specific research.

  • Data and evaluation: engineers building the pipelines, benchmarks and evaluation systems that tell you whether a model is actually improving, rather than just producing interesting demos.

  • Technical leadership: people who can set architecture, make hiring decisions, mentor newer engineers and stop your most senior ICs becoming accidental managers by default.

What Catches Companies Out? 

Slow Process

Capacity is typically the biggest blocker during the recruitment process. More specifically, this is interview capacity, the piece of the puzzle that has the potential to move the slowest. 

When your team is expected to review technical tasks, meet candidates, mentor their colleagues, and create progress plans, all while getting on with their day job, you’ll end up with plenty of people in the process and nowhere near enough time to move them through it. 

There’s extreme competition for talent in the AI space, and since the engineers there sport some of the lowest tenures in tech, it’s important your process moves quickly and doesn’t work against you. 

Slow Process = Candidate dropouts, brand damage, acceptance rates fall, momentum disappears, the team stays under-resourced for longer, employer reputation takes a hit.

We like to use these methods to help reduce friction and speed up the process: 

  • Service level agreements need to get signed off by everyone. If five hiring managers all have a different idea of what a good timeline looks like (or even what a Senior ML Engineer does), you’ll waste time interviewing the wrong people.

  • Interviewing is work and it should be treated that way. It’s best to ringfence some time in people’s calendars rather than squeezing interviews around day jobs.

  • Stagger the roles. If you release everything at once, you lose control of prioritisation. Founders often assume that moving faster means opening everything immediately, but proper sequencing is what makes high-volume hiring work.

  • Decide who owns the hiring decisions. It’s not always the case, but long feedback loops are usually ownership problems, not recruitment problems. 

Keeping your eye on a few basics like can ensure the pipeline doesn’t get a chance to stall, at least, not because of your setup. 

Exhausted Talent Pools

Volume hiring in a space like AI requires searching a little further afield. Opening up more channels and setting up a sturdy cross-border hiring plan will give you access to a much wider pool without circling the same candidates. 

One of the easier assumptions to make with hiring at scale is that it’s as simple as increasing the amount of outreach. Specialist talent pools are relatively small, however, so once you start zooming in on niche corners of the talent marketing, an overreliance on one or two sourcing channels will leave you competing in the place as everyone else. 

Given the uptick in companies racing to build proprietary tech, ‘everyone else’ now really means everyone else. 

The top targets are passive candidates who require a structured, diversified approach to access. When we start sourcing for a larger hiring programme, we’ll: 

  • Built our own routes into passive candidate networks. DeepRec.ai’s networks are supported by a dedicated AI and Deep Tech community that we’ve been building since before the day we launched our business. Through our events programme, various podcast series and previous hiring mandates, we’ve built an international talent community that’s engaged, passive, and highly specialised.

  • Ensure that domain specialists lead the hiring projects. One of the perks of being an AI-native recruiter is that you get to build highly technical recruitment teams. Our consultants focus on defined markets and as a result, have a granular knowledge of the terminologies and technologies in their specialisms. This makes initial outreach much easier, and means we can keep the CV-to-interview ratio strong at the same time.

  • Lean on networks from across Trinnovo Group. As part of Trinnovo Group, we can work alongside Trust in SODA when a build starts touching wider technology infrastructure, or Broadgate Search where governance, risk and compliance expertise becomes relevant. The same structure gives us access to the operational support behind larger projects. We retain the specialist focus of a boutique consultancy while enjoying the resources of a global staffing firm.

  • Make talent mobility a part of the plan. With teams operating across the UK, Ireland, Switzerland, Luxembourg, Germany and the US, DeepRec.ai can support hiring beyond one domestic market when the brief demands it. That reach is backed by regulatory licensing, including SECO in Switzerland and AUG in Germany, plus an in-house compliance function that can handle the contracts, mobility and employment admin that comes with cross-border hiring. For the employer, that means access to a wider talent pool without taking on all of the operational burden themselves.

  • Use talent intelligence to steer the search. Every hiring programme generates data. We combine that with our own sourcing technology, market mapping and live candidate intelligence to understand where talent sits, how markets are moving and which search strategies are producing the strongest results. That means we can spend less time guessing and more time speaking to the right people.

The aim is to create room to find the right people without compromising on quality because the first-choice talent pool has dried up. 

There’s no need to panic about running out of available candidates when you can expand the boundaries early on. The sooner the better too; it means you keep the hiring plan in your hands without letting the market dictate your next move.

Losing Momentum

Hiring plans aren’t static and they tend to age pretty quickly, especially when you scale them up. For example, roles that looked business critical at the beginning of the project might become less urgent as the other teams start to fill up. 

In this context, Momentum is the ability to keep moving through the hiring process, even as the business around it changes. Momentum is captured by hiring velocity, or the rate at which candidates can keep progressing from first contact through to offer and onboarding.

To keep momentum from stalling on scaling projects, we’ll usually: 

  • Keep revisiting the order of hires. Every few weeks we’ll sit down with hiring managers and revisit the roadmap. As teams grow and priorities evolve, it’s not unusual for one role to become significantly more valuable than another. The hiring plan should be able to reflect that.

  • Measure hiring velocity, not just hiring volume. We keep a close eye on how candidates move through each stage of the process, where decisions are slowing down and where offers begin to lose momentum. Small delays have a habit of becoming much bigger problems when you’re hiring at scale.

  • Feed market intelligence back into the plan. Every conversation reveals something more about the market. Candidate expectations, salary movement, notice periods and emerging technical trends are all ammunition for your hiring process. Use it to get ahead of the competition.

  • Keep the right people close to the project. Larger hiring programmes involve engineering leaders, founders, finance, talent and people teams. Regular reviews help everyone stay aligned on priorities.

  • Leave room for the unexpected. Whether it’s a new customer, a product milestone or a shift in technical priorities, accounting for a little (or a lot) of flexibility in the roadmap makes it easier to respond to the inevitable changes that head your way. 

When Does it Make Sense to Bring in a Talent Partner? 

Most companies don’t need an embedded talent partner 100% of the time. There does come a point where hiring starts to become a process that the business needs to support, as opposed to a process that sits alongside everyone’s existing priorities. 

Here are some examples of real-life situations where we’ve been brought in as an embedded talent partner: 

Situation: Engineering teams are spending more time interviewing than working on the tech.

How We Approached It: Our embedded hiring team took ownership of the sourcing, screening, interview coordination, stakeholder management, and candidate communication, which meant the technical teams could focus on managing their candidates rather than the process.

Situation: A Series A GenAI startup was expanding into a new market following a funding round. The founders had a strong technical vision and a solid product, but no internal hiring capable of taking on the increasing hiring demands. 

How We Approached It: We embedded a dedicated hiring team, agreed the order of hires with the leadership group, built the interview process around technical availability and delivered the programme alongside the founders without asking engineering managers to become full-time recruiters.

Situation: An AI platform company needed to grow across research, infrastructure and product engineering at the same time, with hiring priorities changing as each part of the team developed.

How We Approached It: We built the delivery team around the full technical lifecycle so the same programme could cover research scientists, applied ML, infrastructure, MLOps, platform engineering and technical leadership without handing different parts of the build to disconnected suppliers. With DeepRec.ai’s domain experts leading each specialism, we gave the client one hiring partner across the programme, while ensuring that we had the appropriate expertise covering each role.

Situation: An established technology business found itself needing to make a large number of specialist AI hires after launching a new product line. The internal talent team was already operating at full capacity, but bringing in permanent recruiters for a short-term programme didn’t make commercial sense.

How we approached it: We stood up a dedicated project team that operated alongside the client’s existing talent function for the duration of the programme. Delivery, reporting, stakeholder management and market intelligence all sat with DeepRec.ai, allowing the internal team to maintain business-as-usual hiring while the specialist build ran in parallel.

How Do You Know When it’s Working? 

It’s tempting to say that it’s worked once you’ve filled all the vacancies, which is partially true, but it’s not the whole picture.

A successful hiring programme should leave the next stage of growth feeling easier than the last. The process should be taking up less engineering time, the market should feel more familiar than it did at the beginning, and the business should have a clearer understanding of how to tackle the next wave of hiring. We’ve seen that happen on embedded programmes where leadership teams have regained weeks of recruitment capacity, and on highly specialised searches where widening the market has turned previously stalled hiring projects into successful ones.  

We did this at MOTOR AI, where our embedded team took ownership of the full hiring process during a critical growth period (right after they secured a funding round). Across the project, we removed more than 50 hours of recruitment admin and the equivalent of three months of recruitment workload from the leadership team. 

Planning to Scale Your Team? 

If you’re working with frontier technology and you’re looking for an AI-native talent partner who speaks the technical language, DeepRec.ai have the networks and knowledge to support you.

We partner with AI and Deep Tech businesses through executive search, retained search, embedded hiring, contract recruitment and dedicated project teams, tailoring our approach to the stage, scale and complexity of each hiring programme.

You might need an embedded team for six months, a retained search for one critical leadership hire, or contract support while the permanent team catches up. We can scale that support up or down as the brief changes, so you’re not stuck paying for, or working within, a model that no longer fits the job.

Reach out to the DeepRec.ai team directly to start the conversation about your hiring plan, the areas proving hardest to fill, and the delivery model that makes the most sense for the stage you’re at: Partner with DeepRec.ai.