Inside the 2026 ML Engineer Hiring Process at Top UK Tech Companies: What Monzo, Deliveroo and Wise Are REALLY Looking For

Inside the 2026 ML

Engineer Hiring Process at Top UK Tech Companies

If I've learned anything from my conversations with technical recruiters this quarter, it's that the ML engineer hiring landscape has transformed dramatically. What worked in 2025 is already outdated.

Here's the reality: While demand for ML talent in the UK is at an all-time high. According to Indeed, AI/ML engineer job postings grew 86% year-on-year in the UK in 2025, placing the role in the top 10 fastest-growing jobs in the country. Generative AI roles saw even steeper growth globally, with a 50% increase between 2022 and 2024 according to LinkedIn data. Fewer broad 'AI' roles exist; what's being hired for now are focused, production-ready ML specialists with clear ownership expectations. The disconnect is a misalignment between how candidates present themselves and what employers are screening for.

The Current ML Hiring Landscape in the UK

The UK tech scene has matured significantly since the post-pandemic boom. Companies like Monzo, Deliveroo, and Wise have refined their ML engineering recruitment to focus on specific signals that predict on-the-job success rather than traditional credentials.

With Indeed's 2025 UK Best Jobs data puts the average salary for AI/ML engineers at £68,560, with senior roles at specialist firms commanding significantly more. Salary growth for ML specialists has outpaced the broader tech sector, though the 26% year-on-year increase cited in some reports reflects a narrow subset of senior London-based roles rather than the wider UK market. At that level of investment, the bar for making it through the technical screen has risen accordingly.

What's Changed in 2026?

• Specialisation over generalisation: Companies now hire for specific ML domains rather than "full-stack ML engineers"
• Applied ML focus: Academic credentials carry less weight than demonstrable business impact
• Ethical AI evaluation: Every major UK tech firm now includes ethics scenarios in their interviews
• Team-fit assessment: Collaborative coding sessions have replaced isolated algorithmic challenges

The Monzo ML Engineer Hiring Blueprint

Monzo's process has become well-regarded in the industry. Recruiters I've spoken to consistently cite it as one of the more structured and candidate-respectful processes they've encountered.

Here's their current process:

1. Initial Screening

• Take-home challenge: A real-world problem using Monzo's anonymised data
• Key assessment criteria: Solution architecture, data preprocessing approach, and model explainability
• Time allocation: 4-6 hours (they explicitly tell candidates not to spend more)

2. Technical Interview

• Format: 90-minute panel with two ML engineers and one product manager
• Focus areas: Model selection rationale, evaluation metrics justification, and performance optimization
• Insider tip: Candidates who connect their technical decisions to business outcomes consistently stand out in the assessment.

3. System Design Challenge

• Real-time exercise: Designing an ML system that scales with Monzo's customer base
• Evaluation criteria: Considerations for data pipelines, monitoring, and infrastructure costs

4. Cultural and Ethics Assessment

• Scenario-based questions: How would you handle biased training data?
• Team collaboration simulation: Working with product and data stakeholders

Deliveroo's ML

Talent Acquisition Strategy

Deliveroo has overhauled their ML hiring process in 2026, moving to what they call their "Impact-First Framework."

What makes their process unique:

• They start with a business problem rather than a technical challenge
• Candidates present their approach to cross-functional teams
• They assess communication skills from day one of the process

According to Deliveroo's Head of AI Recruitment (whom I spoke with last month), they're looking for engineers who can "translate business metrics into model performance metrics and back again."

Red Flags in Their Process

• Theoretical focus: Candidates who can't connect ML theory to business outcomes
• Solo mindset: Inability to explain complex concepts to non-technical stakeholders
• Tool fixation: Being married to specific frameworks rather than selecting the right tool for the problem

The Wise ML Engineering Bar

Wise has perhaps the most structured ML hiring pipeline I've seen. Their process is designed to simulate the actual workflow of their ML teams.

Technical Assessment Components

• Data quality analysis: Identifying issues in messy financial datasets
• Feature engineering: Creating predictive features from transaction data
• Model lifecycle management: Discussing deployment, monitoring and maintenance
• A/B testing design: Planning experiments to validate ML solutions

What's fascinating about Wise is their emphasis on what they call "ML systems thinking" rather than algorithmic complexity. One recruiter told me: "We'd rather hire someone who builds robust, maintainable systems than a candidate who can squeeze an extra 0.1% of accuracy using complex approaches."

Common Themes Across Top UK Tech Companies

After analyzing the ML hiring processes at 12 leading UK tech companies in 2026, clear patterns emerge:

Technical Screening Evolution

• From: Algorithm-focused whiteboarding exercises
• To: Applied problems using real (anonymized) company data

Skills in High Demand

• MLOps expertise: Deployment, monitoring, and maintenance knowledge
• Responsible AI practices: Bias mitigation and model explainability
• Data quality assessment: Working with imperfect, messy datasets
• Business translation: Connecting ML metrics to company KPIs

Companies have significantly reduced their focus on academic credentials. In fact, having published research papers was ranked 8th in importance among hiring factors, down from 3rd place just two years ago.

How Recruiters Can Adapt to the New ML Hiring Reality

If you're handling ML engineer recruitment, here are concrete steps to align with what top companies are looking for:

1. Revamp your technical assessments to include business context and cross-functional collaboration
2. Focus candidate screening on practical experience over theoretical knowledge
3. Train your interviewers to evaluate both technical depth and communication abilities
4. Include ethics scenarios that reveal a candidate's approach to responsible AI
5. Create clear evaluation rubrics that remove unconscious bias from the process

Tools That Can Help

Several platforms have emerged to support the specialized ML recruitment process. The OHub's specialized ML recruitment tools have become particularly popular for tech companies wanting to improve their conversion rates from application to hire.

Building Your 2026 ML Recruitment Strategy

The companies seeing the most success are those that have fundamentally reimagined their ML hiring process to match the evolving nature of the work itself.

Consider these strategic elements:

1. Create simulation-based assessments that mirror actual ML work at your company
2. Involve product and business stakeholders in the interview process
3. Evaluate collaborative potential through pair programming sessions
4. Assess adaptability by introducing new requirements mid-challenge
5. Measure communication skills by having candidates explain their approach to different audiences

Tech companies that give ML candidates early, direct access to their engineering team, through video introductions or informal calls, tend to see stronger offer acceptance, particularly for senior roles where cultural fit carries more weight than compensation alone.

The Signals That Really Matter

After all my conversations with hiring managers and successful candidates, here's what actually predicts ML engineer success in 2026:

• System thinking over algorithm optimization
• Business acumen alongside technical depth
• Collaboration skills with diverse stakeholders
• Ethical considerations in model development
• Pragmatic problem-solving over theoretical purity

What's Next for ML Recruitment?

Looking ahead, we're seeing early signs of even more evolution in the ML hiring process. Companies like DeepMind are experimenting with collaborative challenges where candidates work with existing team members to solve problems.

The most forward-thinking organizations are also incorporating ML tools into their own hiring process, using structured assessments that reduce bias and predict job performance more accurately.

For recruiters and hiring managers navigating this landscape, staying current with these practices is essential for securing the talent that will drive the rest of 2026 and beyond.

Ready to elevate your ML recruitment process? Explore specialized technical recruitment solutions designed specifically for cutting-edge engineering roles in the AI and ML space.

What ML recruitment challenges are you facing? I'd love to hear your experiences in the comments below.