Hiring guide
How to Hire Machine Learning Engineers in 2026
The market in 2026
Every company is suddenly “an AI company”, which makes genuine machine-learning talent the most contested hiring market of the year. The candidates who can take models to production, not just fine-tune a demo, are heavily outnumbered by the roles chasing them. That pressure holds across the industries we hire for, not just the obvious AI-native startups, so it shapes how you have to hire wherever you sit.
Screen for production ability, not demos
The single most important filter: can this person get a model and keep it in production? Look for:
- MLOps experience: deployment, monitoring, retraining, not just notebooks.
- Production LLM work: retrieval, evaluation, and cost/latency engineering are 2026’s most-requested skills.
- Strong software fundamentals: the best ML engineers are strong engineers first.
- Data engineering underneath: pipelines and quality are what separate AI teams that ship from ones that demo.
A real technical screen is non-negotiable here; a polished CV or a Kaggle rank tells you little about production ability. We screen every candidate ourselves.
Sell the problem, not just the salary
AI engineers choose roles on compute, data access, publication freedom and the ambition of the problem, as much as pay. Be specific about what they’ll build and why it matters. A compelling problem beats a slightly higher number more often than you’d think.
Pay to compete
AI/ML engineers earn a clear premium, and if you’re competing with well-funded labs for the same person, that premium isn’t optional. See the UK tech salary report and our AI, ML & data recruitment page for current ranges.
Move fast
The strongest people are gone quickly. Short loops, fast feedback, real people. Building an AI or ML team? Talk to us for a technically screened shortlist.
FAQ
Frequently asked questions
What should I look for when hiring a machine learning engineer?
The ability to take models to production, not just fine-tune a demo. Look for real MLOps experience (getting and keeping models live), strong software fundamentals, and, in 2026, production LLM experience: retrieval, evaluation, and cost/latency engineering. Strong data engineering underneath is what separates AI teams that ship from ones that demo.
How much do machine learning engineers earn in the UK?
AI/ML engineers command a clear premium over general software engineers, roughly 8-15% in London on 2026 benchmarks, with those combining LLM and MLOps experience earning £10k-£25k above peers. Senior ML engineers in London typically earn £90k-£125k base.
Why is it so hard to hire AI engineers right now?
Demand has exploded while the pool of people who can actually ship models in production is small. Candidates weigh compute, data access and the problem itself alongside salary, so the offer is about more than the number, and the strongest people have multiple options.
Is this only a fintech or big-tech problem?
No. Genuine ML hiring pressure now shows up across the industries we hire for, from fintech and SaaS to marketplaces, wherever a team is trying to ship models rather than just demo one.