Machine Learning Engineer

Location: London (Hybrid)
Reporting to: Head of AI 

The role 

We’re looking for a talented Machine Learning Engineer to join Rightmove’s growing Data, Analytics & AI team and help scale our ability to deliver innovative AI-powered solutions. You’ll design, deploy, and optimise AI and machine learning systems that are reliable, maintainable, and measurable. 

You’ll design, deploy, and iterate on AI-powered features that are reliable, maintainable, and—most importantly—impactful. You will bridge the gap between Data Science experiments and production-grade products, ensuring that our AI solutions solve real-world problems for home hunters and partners. 

A typical week as a Machine Learning Engineer might involve 

• Working with data scientists to take models from initial development to production-grade features with scalability and user-experience in mind. 

• Building "scrappy" internal tools or prototypes to validate AI concepts before scaling them into full production pipelines 

• Designing and maintaining the ML pipelines (training, deployment, monitoring) that keep our product-facing models running smoothly. 

• Translating model performance into business metrics (e.g., accuracy vs cost/latency trade-offs) 

• Collaborating with product, design, and engineering teams to deliver AI and ML features that directly impact customer experience 

• Automating feature engineering and data pipeline processes, ensuring reproducibility and auditability 

• Implementing monitoring and observability to detect drift, bias, and performance degradation, and setting up rollback/recovery processes 

• Using MLOps tools (e.g., Vertex Pipelines/Kubeflow, W&B) for experiment tracking, model registry, and automated deployment 

• Leveraging Docker/Kubernetes and workflow orchestration tools (Airflow, Prefect, Dagster) 

We’re looking for someone who 

• Has experience shipping and maintaining AI or ML systems in production, ideally including large scale batch inference pipelines and online inference microservices, with retraining and monitoring 

• Understands the full ML lifecycle and can design for long-term resilience without losing sight of the immediate product goal. 

• Has strong programming skills (Python, SQL) and experience with ML frameworks (PyTorch, TensorFlow, Scikit-learn) 

• Experienced with cloud platforms (ideally GCP: BigQuery, Vertex AI, Dataflow) 

• Is comfortable with CI/CD, Git, and containerisation/orchestration (Docker, Kubernetes) 

• Has strong communication skills to explain trade-offs and influence both technical and non-technical colleagues 

• Is proactive, detail-oriented, and motivated to learn emerging ML engineering tools 

We would love someone to have any of the following 

• Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Data Science, or a related STEM subject 

• 3+ years of experience as an ML Engineer, Data Engineer, or similar 

• Hands-on with model monitoring, observability, and retraining pipelines 

• Exposure to feature stores, registries, and experimentation frameworks 

• Familiarity with business-driven metrics and experience balancing ML performance with commercial goals 

• Experience with LLM and Agentic AI frameworks (e.g. ADK, LangChain, AutoGen)