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
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