Machine Learning Engineer (AI Start-Up) - Multiple Roles & Differing Seniorities - £70k - £110k

Machine Learning Engineer (Multiple Roles & Differing Seniorities)4 Days on site in London£70-£110k + Onsite Food 🍕Our client builds the intelligence layer behind one of the UK's fastest-growing on-demand labour platforms, the kind of company that quietly works out who needs to be where, and when, before anyone has to ask. As their machine learning engineer, you'll work alongside data scientists, engineers, and product folks to build models, shape data pipelines, and power decisions that ripple out across a huge, fast-moving network of sites. Some weeks you're deep in a forecasting model, others you're fixing something in production at 4pm on a Thursday because that's just how it goes.Your work will sit behind the apps and dashboards used daily by major operators, turning a messy tangle of data into decisions that actually hold up. You'll play a real part in scaling the platform as the business expands.This is a collaborative, in-person team (4 days per week), most days spent together in the office because some conversations just work better face to face.What you'll actually be doing:Building and maintaining machine learning models that power customer-facing apps and internal toolsDesigning data architecture that won't make future-you want to quitBuilding models that forecast demand and help match the right people to the right shifts, at the right timeBuilding ETL pipelines pulling from a wide range of APIs and sources, and making sense of the messWorking closely with data scientists, engineers, and internal stakeholders to understand what data is actually needed and whyWriting documentation people will genuinely use, and catching pipeline issues before they turn into pagesYou might be a good fit if you have:Strong foundations in maths, stats, and modelling, with an eye for patterns in messy real-world dataHands-on experience shipping production-grade ML, ideally in demand forecasting, computer vision, or optimisationSolid ML Ops experience and a real interest in good data architectureComfort with data modelling, database design, and normalisationFluency in Python and SQL, ideally with exposure to Airflow, PyTorch, or SparkWorking knowledge of supervised and unsupervised learning, and judgement on when to reach for whichSome cloud experience (AWS or similar), ideally including managed ML servicesWillingness to get hands-on with backend work to help ship models into productionAn appreciation for data versioning, CI/CD, and not breaking things on a Friday afternoonWhat you'll get:Private medical insuranceA close-knit, down-to-earth teamReal equity in a business that's genuinely growingA relaxed, informal office cultureFood and snacks taken care of, especially on the long daysThe chance to build something people actually rely onThey're a growing team who like solving real, gritty problems with genuinely good tech, want to move fast, and don't take themselves too seriously along the way.Come build something people actually rely on.