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

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 tools
• Designing data architecture that won't make future-you want to quit
• Building models that forecast demand and help match the right people to the right shifts, at the right time
• Building ETL pipelines pulling from a wide range of APIs and sources, and making sense of the mess
• Working closely with data scientists, engineers, and internal stakeholders to understand what data is actually needed and why
• Writing documentation people will genuinely use, and catching pipeline issues before they turn into pages

You might be a good fit if you have

• Strong foundations in maths, stats, and modelling, with an eye for patterns in messy real-world data
• Hands-on experience shipping production-grade ML, ideally in demand forecasting, computer vision, or optimisation
• Solid ML Ops experience and a real interest in good data architecture
• Comfort with data modelling, database design, and normalisation
• Fluency in Python and SQL, ideally with exposure to Airflow, Py Torch, or Spark
• Working knowledge of supervised and unsupervised learning, and judgement on when to reach for which
• Some cloud experience (AWS or similar), ideally including managed ML services
• Willingness to get hands-on with backend work to help ship models into production
• An appreciation for data versioning, CI/CD, and not breaking things on a Friday afternoon

What you'll get

• Private medical insurance
• A close-knit, down-to-earth team
• Real equity in a business that's genuinely growing
• A relaxed, informal office culture
• Food and snacks taken care of, especially on the long days
• The chance to build something people actually rely on

They'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.

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