[All remote jobs](https://jobicy.com/jobs.md)Open role[![Sona logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/740d9e4e-221.png)](https://jobicy.com/company/sona.md)Remote opportunity at[Sona](https://jobicy.com/company/sona.md) # Senior Machine Learning Engineer Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/sona.md)Share17 Aug 2026Published30Listing views3Application actions16 Sep 2026Apply before Opportunity details ## About this role. AI SummarySona is hiring a Senior Machine Learning Engineer to help scale a demand-forecasting platform for restaurant chains. The engineer will work within a two-person ML team, producing half-hourly demand forecasts in a complex operational setting with feedback loops and variable data quality. The role owns client launches end-to-end, including data validation, model selection, UAT, go-live, monitoring, and client communication. Candidates need strong production ML and Python skills, ideally including time-series forecasting, and should be comfortable using AI development tools as part of daily work. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis is a highly demanding senior role because it combines production forecasting expertise with direct ownership of client launches, operational reliability, and post-launch performance. Success requires sound technical judgement under messy-data conditions as well as the ability to connect model outputs to real business outcomes. ## Salary analysis Estimated compensation compared with the broader UK market for similar roles. Estimated job medianMarket rate£102,500UK market range£90k–£120k0£132k AI insightThe posting explicitly states a full-time salary range of £95,000–£110,000. The offered midpoint is £102,500 per year. This is broadly aligned with a competitive UK market range for a senior production-focused machine learning engineer with forecasting, deployment, and client-facing ownership responsibilities. ## Core skills Skills and capabilities most closely associated with this opportunity. [Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[Time-Series Forecasting](https://jobicy.com/jobs?search_keywords=Time-Series%20Forecasting.md)[Demand Forecasting](https://jobicy.com/jobs?search_keywords=Demand%20Forecasting.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[NumPy](https://jobicy.com/jobs?search_keywords=NumPy.md)[scikit-learn](https://jobicy.com/jobs?search_keywords=scikit-learn.md)[MLflow](https://jobicy.com/jobs?search_keywords=MLflow.md)[Docker](https://jobicy.com/jobs?search_keywords=Docker.md)[Google Cloud Platform](https://jobicy.com/jobs?search_keywords=Google%20Cloud%20Platform.md)[MLOps](https://jobicy.com/jobs?search_keywords=MLOps.md) Cover letter sampleDear Hiring Team, I am excited to apply for the Senior Machine Learning Engineer role at Sona. My experience delivering production machine-learning systems, particularly forecasting solutions operating on imperfect real-world data, aligns closely with your need to scale reliable client-facing demand prediction products. I would bring strong Python and scientific ML expertise, pragmatic model-selection judgement, and an end-to-end approach spanning data validation, deployment, monitoring, and iterative improvement. I am also comfortable translating technical performance into operational outcomes for non-technical stakeholders and working closely with clients during launches. Sona’s focus on useful, deployed AI for frontline businesses is especially compelling, and I would welcome the opportunity to help build dependable systems that improve customer operations. Copy Sample interview questionsHow would you take a demand-forecasting client from raw data to a successful production launch?I would begin by auditing source completeness, timestamp consistency, granularity, target definitions, and leakage risks. I would establish a simple, robust baseline, use time-aware validation splits, and select a model based on both forecast accuracy and operational reliability. Before go-live, I would agree acceptance criteria with the client, run UAT against realistic scenarios, and ensure monitoring and rollback procedures are in place. What would you do if forecast accuracy deteriorated materially after deployment? I would investigate whether the issue is caused by data quality, distribution shift, a changed operating process, or a weakness in the model assumptions. I would compare live inputs and prediction errors with historical baselines, communicate the likely business impact clearly, and deploy a safe mitigation such as a fallback forecast or retrained model where appropriate. I would then document the root cause and add monitoring to detect the same failure mode earlier. How do you preserve evaluation integrity in a time-series forecasting problem? I use rolling or expanding-window backtests so that each evaluation reflects the information that would have been available at prediction time. Features are generated strictly from past data, and I check that calendar, promotions, and operational inputs would genuinely be known in advance. I also evaluate across stores, periods, and unusual demand conditions rather than relying on one aggregate metric. How would you communicate model performance and uncertainty to a non-technical restaurant operator? I would explain the forecast in terms of the operational decision it supports, such as staffing or food preparation, rather than beginning with model architecture. I would describe uncertainty, expected error ranges, and the practical trade-offs of over- versus under-forecasting. Regular feedback from the stakeholder would help validate whether the output is useful and reveal operational changes that the model should account for. How do you use AI development tools effectively while maintaining quality in production ML systems? I use AI coding tools to accelerate routine development, explore implementations, generate tests, and improve documentation, while retaining responsibility for design, validation, security, and correctness. For ML systems, I verify generated code carefully, especially around data transformations, leakage, evaluation logic, and deployment behaviour. The goal is faster iteration without weakening engineering discipline. 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](https://www.sona.ai/careers). 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) * Share options * 35 days annual leave (25 days standard plus 10 flexible public holiday days) * Extra day of leave for every year of service * Pension contributions matched up to 5% * Comprehensive health insurance * Enhanced parental leave & pay * Salary sacrifice childcare scheme (Workplace Nursery) * Co-working space stipend for those based outside London * Annual all expenses paid team retreats * The latest Macbook and equipment budget for your home office * Professional development budget * Unlimited free books Note: this represents a typical benefits package for a UK-based, full-time employee. Exact details may vary based on location and employment type but we try to be as fair as possible to all of our team members. Please ask your contact in the Talent team to clarify the available benefits for you. 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