Running a frontline business is an operational puzzle most software has never touched. Shift-by-shift labour costs, compliance that changes by region and by role, and margins thin enough that a badly built rota can sink a quarter. It's a harder problem than most white-collar SaaS, and yet frontline sectors employ 80% of the global workforce and have received about 1% of the last decade's software investment.

We think that's because the problem needed AI, not just better software, before it was solvable. Frontline organisations sit on enormous amounts of operational data. Used properly, that data lets agents take on real parts of running the business rather than just reporting on it. That's what we're building at Sona, and it's early enough that the systems you build now will still be foundational in three years.

We've raised over $100M from N47, Felicis, Gradient and Northzone, signed more than 100 enterprise customers across the UK and US, and opened offices in London, New York, Austin and Lisbon. More on working at Sona here.

About the Role

You'll join a two-person ML team and a forecasting system making half hourly demand predictions across diverse targets for multiple restaurant chains. Our forecasting models enter into a complex environment with key machine and human decisions being made on their predictions, facing feedback loops and a highly variable environment. The system works - the challenge now is scaling it from a handful of clients to 100s.

You'll own client launches end-to-end: validating data, selecting models, running UAT, going live, and monitoring performance afterwards. You'll join client calls, build relationships, and understand what actually matters on the ground - not just whether the model is accurate, but whether the kitchen prepped the right amount of food.

You'll love this role if:

You enjoy taking ownership of the product and outcome end-to-end. Machine learning at Sona is a success if we have happy clients running successful businesses as well as the models which are best in industry
You have a focus on solving the problem and when given the choice between "complicated and shiny" vs "get something simple in front of a user", you choose the latter
You're excited by working with our industry experts to really understand what's happening in our client's businesses and the realities of working there
You see beyond the data to the world that resulted in this data generating process, the issues that come with it and the opportunity that it gives us
You're experienced in and excited by taking a machine learning project from business idea to deployed production system
You default to AI tools for development and you're excited by what they can achieve for ML. You use Claude Code, Cursor, or equivalent daily - not as a novelty, but as your standard working mode

Our role won't be for you if:

You're hoping to do research and publish research papers as a key element of the work that you do
You're looking to move into a less technical, more managerial role
You're keen to get your hands on fancy new technology X and apply it to something
You prefer to work on one thing and make it perfect before moving on - the role requires pragmatism, parallelism, and iterative improvement

Requirements

You'll need these skills/experience to be successful:

Production ML experience, with a track record of deploying ML systems that handle messy data, fail gracefully, and need monitoring
Strong ML fundamentals - you can reason about trade-offs in practice, explain the "why" behind feature and model choices, and make good judgement calls when something unexpected happens
Client-facing deployment experience - you've personally owned an ML deployment end-to-end and are comfortable on calls with non-technical stakeholders
Strong programming skills in Python, including the ML/scientific Python stack (e.g. numpy, scikit-learn)
Daily use of AI development tools (Claude Code, Cursor, Copilot or equivalent) as your default working mode

It would be great if you have experience in some of these areas too:

Forecasting, time-series, or demand-planning - someone who understands lag features, calendar effects, and evaluation integrity intuitively will ramp significantly faster
Our stack: Python, scikit-learn, MLflow, Docker, GCP
A small team where ownership is wide and context-switching is normal

Benefits

Salary: £95,000-£110,000
Fully remote (European timezones)
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Senior Machine Learning Engineer at Sona