Senior Machine Learning Engineer, Sona

• 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. You use Claude Code, Cursor, or equivalent daily - not as a novelty, but as your standard working mode
• 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 why certain features matter more than model selection, 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

Desirable

• You'll join a two-person ML team that has built a production forecasting system running daily half-hourly predictions across 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