Hiring nowRetail, ecommerce, advertising and sales intelligence

AI Personalisation Engineer

Also called Personalisation ML Engineer

An AI Personalisation Engineer builds and monitors the systems that decide what each user sees. They improve discovery and conversion while keeping exposure, privacy and fairness under control.

What they do

They build and run the service that ranks, recommends or tailors content, offers and messages for individual users. They work with search and recommendation systems, CRM data and analytics. They design experiments, read the results with care and keep a clear record of each test. They watch for feedback effects, where the system shows people more of what it already showed them and narrows what they see. They check exposure across sellers, products and user groups. They set limits on the data used, working with privacy and legal colleagues. They also monitor live behaviour and fix drift, bad data and odd results.

What a strong hire delivers

A strong hire gets a working personalisation service into production, with monitoring that the team trusts. They leave a clean set of experiment records showing what was tried, what changed and what was learned. They agree measures of user value with commercial colleagues, and set practical controls for exposure, privacy and fairness. They also spot and reduce at least one feedback problem that was quietly hurting results.

How you can tell they are good

Ask them to walk through a personalisation system they ran live. Good candidates explain how they judged user value, not just clicks. They can describe a test that misled them and why. They talk about feedback loops, sample bias and who was under-exposed. They can explain a privacy limit they set and what it cost. Be wary of answers that only quote uplift on a single headline metric.

Why it is hard to fill

The title is used loosely. Some people called personalisation engineers only tune rules in a marketing tool. Others are strong ML engineers who have never run a clean experiment or thought about fairness. The real skill sits between statistics, retrieval, commercial sense and risk control. Few people have all four. Candidates often come from analytics or marketing science and need to show real production work, so the shortlist is small.

Where to find them

Look in ecommerce, marketplaces, media, travel and retail businesses with large catalogues and returning customers. Typical titles include recommender systems engineer, search and relevance engineer, applied scientist, marketing scientist and experimentation analyst. Some come from commercial analytics and have moved towards modelling. Others come from applied ML teams. Check what they shipped, not the title they held.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for an AI Personalisation Engineer

  1. Tell me about a personalisation system you ran in production. What did you own, and how did you know it helped users?

    A strong answer names their own part, the data used and the measure of user value beyond clicks. They mention monitoring after launch. Worry if they only describe a model, take credit for a whole team, or cannot say how benefit was judged.

  2. Describe an experiment that gave a misleading result. How did you find out, and what did you change?

    Look for honesty and method: interference between groups, novelty effects, weak sample sizes or a wrong metric. They should explain the fix and the record kept. Worry if they claim every test worked or blame the data.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are hiring an AI Personalisation Engineer to build and run the systems that tailor what our users see. The work covers recommendations, ranking and messaging. We want someone who cares about real user value and treats exposure, privacy and fairness as part of the job.

What you will do

  • Build and maintain a personalisation service in production
  • Design, run and document experiments, keeping clear records
  • Monitor live behaviour, drift and data quality
  • Identify and reduce feedback effects in ranking and recommendations
  • Assess exposure and fairness across users, products and sellers
  • Work with commercial, privacy and product colleagues on measures and limits
  • Connect CRM and analytics data to the service responsibly

What you will bring

  • Shipped a personalisation, search or recommendation system that ran live
  • Sound grounding in probability, statistics and experiment design
  • Working knowledge of information retrieval and ranking methods
  • Experience reading commercial and financial results
  • Examples of fairness or impact checks you have carried out
  • Clear written records of past experiments and decisions

Nice to have

  • Experience with CRM-driven messaging
  • Background in marketing science or commercial analytics
  • Experience setting privacy controls in a live system

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Information retrievalAdvancedRetrieve and rank relevant documents or entities
  • Probability and statisticsAdvancedQuantify variability and statistical evidence
  • Commercial and financial analysisWorkingAssess unit economics and value realisation
  • Fairness and impact assessmentWorkingAssess unequal errors, access and downstream impacts

Other roles in retail, ecommerce, advertising and sales intelligence

Hiring nowRetail AI ScientistA Retail AI Scientist builds models that predict customer behaviour or stock outcomes for specific retail decisions. They matter because they tie…Hiring nowAdvertising AI ScientistAn Advertising AI Scientist builds and tests models that decide which ads people see, within privacy and policy limits. They show whether…EmergingSales AI Workflow SpecialistA Sales AI Workflow Specialist designs and tests AI support for sales teams, with clear limits on what it can do. It helps sellers close more…Hiring nowGenerative Engine Optimisation SpecialistA Generative Engine Optimisation Specialist improves how often and how accurately a brand appears in AI assistants' answers. It matters because more…

Related roles in other families

Hiring nowInsurance ML ScientistInsurance, actuarial and claims AIAn Insurance ML Scientist builds risk, claims and operations models that can be validated, explained and defended. They let a company use machine…EmergingAI Actuarial SpecialistInsurance, actuarial and claims AIAn AI Actuarial Specialist tests whether AI methods hold up inside actuarial assumptions and controls. They give the company defensible models and…Hiring nowClaims Automation AI EngineerInsurance, actuarial and claims AIA Claims Automation AI Engineer builds bounded workflows that support and triage insurance claims, with human escalation built in. They help a…EmergingInsurance AI Fairness AnalystInsurance, actuarial and claims AIAn Insurance AI Fairness Analyst tests whether pricing, claims and underwriting models treat different groups of customers differently, and explains…

How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

← Retail, ecommerce, advertising and sales intelligenceAll new job families

Start a search

Hiring an AI Personalisation Engineer?

Tell us the work. We find the people already doing it, whatever their job title says, and you meet them already interviewed for your role.