Machine Learning Engineer

Why this role Our forecasting algorithm and time-series models are already built. What we need now is someone to make them run reliably-ingesting messy client data, maintaining our GCP/BigQuery infrastructure, and shipping outputs consistently as we scale from pilot to production. This role owns that.Who you areYou're a Data/ML Engineer with strong software engineering instincts. You write clean Python, you're comfortable wrangling dataframes, and you know how to build pipelines that don't break at 2am.Required 1-3 years of commercial experience in data engineering, ML engineering, or a similar role ️ Strong Python skills with hands-on pandas/dataframe experience Experience building and running data pipelines (ETL/ELT) with orchestration tools ️ Familiarity with cloud platforms (GCP preferred) and tools like BigQuery ️ Comfortable working with messy, real-world data-CSV ingestion, schema validation, inconsistent formats Able to deploy and monitor models and pipelines in production Can build and maintain dashboards to give the team and clients real-time visibility into model health and forecast outputs You love AI tools and use them to build shit fast-Cursor, Claude, Copilot, whatever gets the job done Clear communicator who documents well and cares about reliability, not just shipping fast Excited to work in a small, fast-paced startup where you'll take ownership and jump in wherever neededDesirable Enough statistics/ML knowledge to retrain, tune, and troubleshoot models-not just wrap APIs ️ Experience owning infrastructure: environments, dependencies, CI/CD, rollbacks Familiarity with containerisation (Docker) or infrastructure-as-code ️ Interest in food retail, fresh food, or supply chain domainsWhat the job involvesData pipelines - Build, maintain, and improve our ETL/ELT pipelines. Ingest messy client data (CSV exports, varying formats, schema mismatches) and transform it reliably for forecasting.Production systems - Deploy statistical/ML models and keep them running at scale. Monitor pipelines and models, set up alerting for failures and data quality issues.Visibility - Build dashboards so the team and clients can see model health and forecast outputs in real time. Surface insights when performance drifts.Infrastructure ️ - Own our GCP environments, BigQuery setup, dependency management, and release processes.Code quality - Write well-structured, tested Python with proper version control practices (branching, PRs, code review).Client onboarding - Own the end-to-end process from receiving a client's first data extract to delivering clean forecast outputs.Collaboration - Work closely with our Head of Data Science to run, maintain, and troubleshoot existing models. Document systems and runbooks clearly.Team & reporting You'll be the first dedicated Data/ML Engineer, working directly with the founder (who loves using AI to build stuff and has a background in UX and product) as their technical sidekick. You'll also collaborate closely with our Head of Data Science and the rest of the founding team. This is a high-impact role where you'll shape how we build and scale our technology, data infrastructure from day one. Similar jobs

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