How to hire machine learning talent

Machine learning (ML) has become a key part of how many businesses make decisions and improve how they operate. As more companies invest in ML, demand for skilled professionals continues to grow.

But hiring the right people isn’t always straightforward. Whether you're in HR, part of an internal talent team, or in a leadership role, it helps to know what to look for and how to approach the process.

In this blog, we share practical advice on how to hire ML talent, which roles are most in demand, and what skills to prioritise.

Why the demand for machine learning talent is growing rapidly

The need for machine learning professionals continues to increase year on year. Businesses that want to stay relevant and innovative are actively investing in data science and ML capabilities.

As a result, the competition for talent is getting stronger, and those who understand the market are better positioned to hire successfully.

If you're planning to grow your machine learning team, it's important to understand why the demand is rising, where it's coming from, and how it's affecting hiring strategies. Here’s a breakdown of the key drivers shaping today’s market.

AI adoption is accelerating across all industries

Machine learning is no longer just for big tech. Retailers use it to personalise customer experiences. Financial services rely on it for risk analysis. Healthcare companies are using it to improve diagnostics. These aren’t future plans—they’re live projects. This growing adoption means businesses of all sizes now need ML talent to support day-to-day operations.

When more organisations invest in AI and ML, it naturally creates more demand for a limited number of experienced professionals. This trend isn’t going anywhere, and if you haven’t started hiring yet, it’s worth planning ahead now.

Businesses need ML to stay competitive

Machine learning isn’t just a ‘nice to have’. It's becoming a core business function. If your competitors are using ML to gain insights, improve efficiency or automate tasks—and you’re not—you risk being left behind. That’s why many businesses are bringing these capabilities in-house.

Hiring the right ML talent allows you to move quickly and use data more effectively. Whether you're building a new product, improving internal processes or simply want better reporting, having the right people on board makes a real difference.

ML roles are critical to digital transformation

Many digital transformation strategies now depend on machine learning. That could include streamlining customer support with natural language processing, or optimising inventory using predictive modelling. These aren’t just technical improvements - they’re business improvements.

To make this happen, you need professionals who can design, build and maintain ML solutions. Hiring for these roles is often one of the first steps in making real progress in your transformation efforts.

They are in high demand worldwide

Machine learning skills are needed across the globe. This means you’re not just competing locally, you're hiring in a global talent market. Strong candidates are approached regularly and will often have multiple opportunities to choose from.

This makes it even more important to move quickly and make competitive offers. It's also why more businesses are turning to specialist recruiters who already have relationships with this type of talent.

Machine learning jobs that are in demand

Before you start the hiring process, it's important to understand which roles exist and how they contribute to your team. Machine learning is a broad field, and not every ML role is the same.

Different positions require different skills and serve different business functions. Here are the most common machine learning roles employers are hiring for in 2025.

Machine Learning Engineers

ML engineers are hands-on builders. They design and train models, integrate them into production systems, and make sure they run reliably. They often work closely with data scientists, software engineers and DevOps teams.

This is one of the most critical hires if you're serious about bringing machine learning into your organisation. They're the ones who turn ideas into real, functioning products.

Data Scientists

Data scientists explore and analyse large data sets to find patterns, test hypotheses and build statistical models. They often take a more exploratory approach than ML engineers and are skilled in communicating insights to stakeholders.

Hiring a data scientist is especially useful if you're still in the early stages of understanding how your data can deliver value. They help shape business decisions by identifying where and how machine learning can be applied.

AI/ML Product Managers

These professionals sit between technical teams and business units. They define the direction of AI-driven features and make sure what’s being built is aligned with company goals.

If you’re developing a product that involves AI, hiring an ML product manager can bring structure to your roadmap. They’ll also help communicate technical capabilities in a language stakeholders can understand.

MLOps Specialists

MLOps roles are focused on operations and getting models into production and keeping them running. They build pipelines, monitor model performance and manage infrastructure.

These are essential hires for scaling your machine learning function. If you're deploying multiple models or want to avoid performance issues down the line, MLOps specialists are worth considering.

Deep Learning Experts

Deep learning experts are a subset of ML professionals who specialise in neural networks. Their work is often focused on image recognition, video, or speech-based tasks.

They’re most relevant in businesses working with unstructured data or building advanced solutions like recommendation engines or computer vision systems.

NLP and Computer Vision Specialists

These are highly focused roles. NLP specialists work with text and language data, such as chatbots or document processing. Computer vision specialists focus on images and video.

Hiring in these areas is usually driven by specific project needs, so it’s important to define exactly what you want them to deliver.

Key skills to look for in machine learning candidates

Understanding what skills actually matter can save you a lot of time when reviewing CVs or running interviews.

It’s not just about academic credentials or ticking off tools on a list. What really counts is how candidates apply their knowledge, solve problems, and align with your business goals.

Here’s what to prioritise when evaluating ML candidates.

Strong foundation in ML algorithms and statistics

Look for candidates who understand how different models work, and more importantly, when to use them. A strong grasp of core concepts like supervised vs. unsupervised learning, regularisation, overfitting and feature engineering is essential.

If someone can explain trade-offs and limitations in plain English, that's a good sign they’re confident with the theory and its application. This foundation makes them better equipped to tackle a range of problems.

Proficiency in programming and ML frameworks

Python is the most commonly used language in the field. Candidates should also be familiar with ML libraries like TensorFlow, PyTorch, Scikit-learn, or XGBoost, depending on the nature of the role.

While tool preference can vary, what matters most is their ability to write clean, efficient code—and their experience using those tools in real projects, not just online courses.

MLOps and model-deployment skills

Deploying machine learning models into live environments is often where things get complicated. Candidates with exposure to version control, CI/CD workflows, containerisation (like Docker), and model monitoring tools bring real value to operational teams.

This skill set becomes especially important as you scale. Without it, even the most accurate models can end up underused or cause problems in production.

Domain knowledge and business acumen

Machine learning isn’t just about building models. It’s about solving problems that matter to your business. When a candidate understands your industry, they’re more likely to focus on the right priorities and spot meaningful opportunities.

You don’t need someone with decades of experience in your sector, but having some familiarity with your customers, products or challenges can make a big difference.

Communication and collaboration

ML professionals often work across different teams. They need to present their work clearly, whether it’s to product managers, leadership, or other stakeholders.

It helps to hire someone who can explain what a model does and why it matters to the business. That kind of clarity makes their work much more useful day to day.

Problem-solving ability and adaptability

The best ML professionals are often the ones who ask the right questions. Look for signs of curiosity and flexibility in how they approach tasks. Have they dealt with messy data? What did they do when a model didn’t perform as expected?

In a field that changes fast, hiring adaptable thinkers can help your business keep pace.

Common challenges when recruiting ML professionals

Even if you know what you’re looking for, there are a few challenges that often come up when hiring machine learning talent. Being aware of these can help you avoid delays, missed hires or misaligned expectations.

Shortage of skilled ML talent

More companies are hiring ML professionals than there are people to fill the roles, particularly at mid to senior level. So, you're likely to be up against other businesses that move quickly and know what they're looking for.

To stay competitive, keep your interview process simple, be flexible where you can, and get back to strong candidates without delay. Working with a specialist recruiter can also help you reach people who aren’t actively job hunting.

Hard to test real-world ML skills

It’s easy to list tools on a CV, but harder to know how someone works in practice. Technical assessments, portfolio reviews or asking for examples of previous projects can help you get a better picture.

Focus on problem-solving, communication, and the ability to make trade-offs—these matter more than perfect code or textbook answers.

Meeting salary and career expectations

Machine learning professionals often expect competitive packages, clear progression, and opportunities to keep learning. That doesn’t always mean the highest salary, but it does mean showing you understand the market.

Our 2025 UK data salary guide includes benchmarks for ML roles, comparisons to 2024, and projections for 2026. It’s a useful tool for budgeting and for aligning your offer with candidate expectations.

Top tips to hire machine learning talent

With the right approach, hiring ML talent doesn’t have to feel like guesswork. Here are a few practical steps you can take to attract and secure the right people.

Support ongoing learning and career growth

ML is an evolving field. The best candidates want to keep learning, whether through training, conferences or personal projects. Be ready to talk about how your business supports development.

That doesn’t mean building an academy. Even a simple learning budget or access to online courses can make your offer more appealing.

Source candidates in ML communities

Not all great candidates are on job boards. Look to GitHub, Stack Overflow, Kaggle and even LinkedIn groups. These communities are often where professionals share work, learn from peers and stay up to date.

If sourcing feels time-consuming, working with a specialist recruiter can help here—we already have these networks in place.

Create technical assessments

Instead of relying on academic history or certifications, ask candidates to complete a task similar to the work they’d do in the role. Keep it realistic and time-bound.

This gives you insight into how they think, code and communicate—without being overly time-intensive for them.

Offer competitive packages

To compete for top ML talent, your offer needs to be aligned with the market. Salaries, flexible working and a clear progression path all play a role.

Sometimes the difference between an accepted offer and a declined one is a small perk, faster process, or better explanation of growth opportunities.

Use a specialist recruiter

Hiring for machine learning is different to general tech recruitment. The terminology, expectations, and networks are more specialised. At Digital Waffle, we’ve built strong relationships with ML professionals across the UK.

If you need to hire fast, reduce time to shortlist, or simply want to improve candidate quality, working with a specialist agency can help you find the right fit, without the usual guesswork.

FAQs about hiring ML talent

It’s normal to have questions when hiring in a specialist area like machine learning. Below are answers to some of the most common questions employers ask us.

What is the best way to recruit machine learning engineers?

Start by clearly defining what you want them to do. From there, build a focused job description, create a streamlined interview process, and use practical assessments to validate skills. If you’re not reaching the right people, a specialist recruiter can help you tap into a wider talent pool.

What is the best way to recruit machine learning engineers?

Start by clearly defining what you want them to do. From there, build a focused job description, create a streamlined interview process, and use practical assessments to validate skills. If you’re not reaching the right people, a specialist recruiter can help you tap into a wider talent pool.

What is the average salary for a machine learning specialist?

Salaries vary depending on seniority and location. In the UK, mid-level ML engineers typically earn between £60,000 and £90,000, while senior roles can exceed £100,000. For a full breakdown, our 2025 UK data salary guide includes benchmarks, year-on-year changes and forecasts for 2026.

Should you hire a freelancer or full-time ML staff?

It depends on your business goals. Freelancers can be great for short-term projects or proof-of-concept work. But if you're looking to build internal capability, a permanent hire is usually the better option. They’ll have more long-term investment in your team and projects.

What qualifications should you look for in a machine learning candidate?

Formal education (such as a degree in computer science, maths or engineering) is useful, but not essential. What matters more is real-world experience—projects they've delivered, problems they’ve solved, and how they communicate technical ideas.

Where can you find qualified machine learning professionals?

In addition to job boards, look to ML communities, GitHub, LinkedIn, Kaggle and academic networks. You can also work with a specialist recruiter like Digital Waffle. We already know who’s available, what they’re looking for, and how to reach them.

Hiring machine learning talent doesn’t need to be overwhelming. Whether you're making your first hire or expanding an existing team, success comes from having a clear brief, understanding the market, and knowing what good looks like.

At Digital Waffle, we’re a specialist machine learning recruitment agency. We know how to spot top ML talent and we’ve built the network to connect them with businesses like yours. Looking to hire in AI? Get in touch with us or submit your vacancy today.