Senior Machine Learning Engineer

Our client is building the ultimate destination for car enthusiasts and buyers alike. As a market leader, they provide powerful search tools, expert car guides, in-depth reviews, and a comprehensive vehicle price guide. Their mission is to empower buyers to find their perfect car quickly and with confidence, while also helping automotive retailers connect with millions of potential customers. The team is transforming the way people buy and sell cars with innovative advertising solutions extending across a broader dealer advertising ecosystem, including well-known international brands.

Required:

• Substantial experience in machine learning engineering with a track record of deploying and maintaining models in a commercial product context.
• Ability to work across the full ML lifecycle making design trade-offs, influencing system architecture and driving quality standards.
• Deep understanding of ML fundamentals including feature engineering, model selection, evaluation, hyperparameter tuning and practical trade-offs.
• Experience designing and operating ML pipelines and infrastructure using Infrastructure as Code principles.
• Practical experience with MLflow or equivalent experiment tracking and model registry tooling.
• Strong proficiency in Python and SQL.
• Hands-on experience with ML frameworks such as scikit-learn, XGBoost, PyTorch or TensorFlow.
• Excellent communication skills to translate technical concepts for non-technical stakeholders and build consensus.

Preferred:

• Experience with Databricks (ML Runtime, Feature Store, Unity Catalog, Model Serving).
• Familiarity with Azure ML services and AKS or container-based model serving architectures.
• Hands-on experience with Terraform and a self-serve mindset towards provisioning and managing infrastructure.
• Exposure to LLMs, generative AI or agentic patterns.
• Awareness of data privacy, security and ML ethics considerations in a consumer-facing, data-rich environment.
• Experience in marketplace, e-commerce or classified advertising domains.

• Identify and deliver high-impact ML use cases across the marketplace, including vehicle price prediction, dealer recommendations, lead scoring, personalisation and search relevance.
• Design, build and deploy ML models and pipelines on Databricks and Azure, following MLOps best practices.
• Own model lifecycle management from experimentation and validation through to production serving, monitoring and retraining, including model testing, shadow deployments and A/B testing where appropriate.
• Build production-ready data models for ML use cases using dbt, with support from Data Engineers and Analytics Engineers.
• Work with real-world marketplace data, contributing to the ongoing journey of strengthening data quality across the platform.
• Implement ML-specific observability covering model drift detection, prediction quality tracking, feature freshness and alerting.
• Optimise ML infrastructure for performance, reliability and cost, leveraging Azure-native services and Databricks capabilities.
• Champion data-driven decision-making and help stakeholders across the business understand the opportunities and limitations of ML.
• Drive ML best practices and standards within your domain and act as an internal consultant to the wider Data Engineering Squad and business teams on advanced analytics opportunities.
• Document domain processes, model decisions, best practices and operational runbooks to ensure knowledge is retained and shared.