Hire ML Engineers

The honest answer is: it depends on the half-life of your Machine Learning surface area. If your roadmap leans heavily on training and evaluation pipelines that are reproducible by someone else and you expect to keep investing in Python over the next 18-24 months, a specialist will out-deliver a generalist on day-30 throughput and incident response.

If your team is smaller than ten engineers, or Machine Learning is one of three or four core technologies, hire a strong generalist who has shipped Machine Learning in anger at least twice. The cross-stack pattern recognition will pay for itself the first time you need to integrate PyTorch with another part of the system.

On Haystack we surface both - filtered by whether the candidate self-identifies as a Machine Learning specialist and verified against their last two roles. Expect to pay around £90k–£120k for a mid-level UK hire, scaling toward £125k–£185k for senior.

A great Machine Learning engineer is not the one with the most stars on GitHub - it is the one who has paged at 3am for a Machine Learning service they wrote, and changed how they build because of it. Across the data, ML and analytics hires we have placed in 2025-2026, the same patterns keep showing up.

Every stack has its own pattern of plausible-sounding answers that fall apart in production. With Machine Learning, these are the patterns that most often correlate with a six-month regret hire on the employer side.

When teams ask us how to evaluate Machine Learning engineers beyond a CV, we recommend a 90-minute paid take-home that mirrors real work, not algorithm puzzles. The brief below is one we have refined with employers hiring data, ML and analytics teams.

Give the candidate a small, intentionally imperfect Machine Learning service that already does training and evaluation pipelines that are reproducible by someone else. Their task is to add a second capability - model serving with latency budgets, batching and fallbacks - while keeping existing behaviour green. Grade in three parts.

By week one, the new Machine Learning engineer should have shipped a small change to production - typically a docs fix, a Python dependency bump or a minor refactor in training and evaluation pipelines that are reproducible by someone else. The goal is to validate the development loop, not to ship anything heroic.

By week two, expect them on the on-call rota in a shadow capacity, pair-programming on at least one feature, and asking pointed questions about why specific Machine Learning patterns were chosen. If they are not asking those questions, the hire is going to plateau.

By day 30, they should own one cleanly-scoped slice of the Machine Learning surface area, have a public ramp-up document, and be the named reviewer on PRs touching that area. Every Haystack employer gets a structured onboarding template - so you are not reinventing the playbook for each hire.