Machine Learning Engineer

We're recruiting on behalf of a fast-growing, well-funded tech company that has spent the last several years transforming how a major service industry manages its workforce. Their platform combines a proprietary on-demand labour marketplace with AI-powered scheduling and demand forecasting; helping some of the UK's most recognised consumer brands stay optimally staffed. They operate across the UK and are starting scale internationally. With a circa 60-person team based in London, they're building genuinely hard-to-replicate technology at the intersection of AI, operations, and real-time logistics.

What the role invovles:

• Designing, building, and owning scalable ML models that power features in both customer-facing products and internal operational tooling
• Developing predictive models for real-time demand estimation and workforce allocation, drawing on proprietary and public data sources
• Owning data architecture decisions — storage, retrieval, and processing — to ensure performant, reliable pipelines at scale
• Building and maintaining ETL workflows that ingest and transform data from multiple third-party APIs and internal systems
• Working closely with data scientists, software engineers, and senior stakeholders to shape and deliver against evolving data requirements
• Deploying and monitoring models in production environments, maintaining documentation and ensuring pipeline reliability

What we're looking for:

• A strong grounding in statistics, mathematics, and modelling — you can interpret complex data signals and translate them into actionable decisions
• Demonstrable experience shipping ML models into production — ideally in demand forecasting, optimisation, or computer vision contexts
• Solid Python and SQL skills, with hands-on experience using frameworks such as Airflow, PyTorch, or Spark
• A good handle on MLOps practices — model versioning, pipeline monitoring, CI/CD, and data quality management
• Familiarity with AWS, particularly services like SageMaker, Lambda, or equivalents — and comfort working with cloud-based ML infrastructure
• Willingness to get hands-on with backend development (Python/Django or Go) to support model integration into live product environments
• Bonus: experience with data lake/warehouse patterns, Terraform, or Apache Airflow at scale