Senior Machine Learning Engineer
Sona
| Company | Sona |
| Category | Engineering |
| Location | London |
| Remote | Remote |
| Employment | Not stated |
| Level | Senior |
| Salary | GBP 95k–110k |
| Posted | 9 Apr 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (ashby) |
Description
3 billion people across the world work in frontline jobs. Yet, despite rising costs and staff shortages, frontline organisations are still left to choose between paper, Excel, and WhatsApp, or decade-old workforce management solutions to take care of the most important part of their businesses - their people.
Enter Sona: the next generation of AI-native, frontline workforce management. We've built an end-to-end platform covering Scheduling, HR, Payroll, and Communications that gives the largest frontline organisations everything they need to staff more intelligently and empower their teams.
In under 5 years, we've already made a deep impact on the lives of over 100k frontline workers and the operation of their organisations, grown the team to 140+, and secured over $100M in funding from notable VC's, including our Series B led by N47 alongside Felicis, Northzone, and Gradient Ventures (Google).
It's a hugely exciting time to be joining the team as we're still small enough that you'll have a significant impact on the company's growth trajectory and culture, yet large enough to have a great structure, experienced leaders and world-class benefits in place.
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