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Senior Machine Learning Engineer
HousefulSenior Machine Learning Engineer designing and deploying ML systems in the UK housing market. Join a company transforming property transactions with intelligent automation and predictive capability.
Tech Stack
Tools & technologiesAirflowAWSCloudPythonPyTorchScikit-LearnSparkTensorflow
About the role
Key responsibilities & impact- Design, build, and deploy end-to-end machine learning pipelines — from data ingestion and feature engineering through to model serving and monitoring in production.
- Own the full lifecycle of ML models: evaluate, iterate, and retire them with the same rigour you bring to building them.
- Collaborate closely with product and engineering teams to frame business problems as machine learning problems, and translate model outputs into product features users actually understand.
- Establish and maintain standards for ML reproducibility, experiment tracking, and model versioning across the Data team.
- Identify opportunities to apply ML across Alto's product suite — surfacing ideas proactively, not waiting to be briefed.
- Work with large, complex datasets drawn from live UK property transactions, ensuring data quality and feature reliability upstream of every model.
- Contribute to a culture of engineering excellence through code reviews, documentation, and knowledge-sharing with data engineers and analysts.
Requirements
What you’ll need- Proven experience building and shipping machine learning models into production environments — not just notebooks and prototypes.
- Strong Python skills and hands-on experience with ML frameworks such as scikit-learn, PyTorch, or TensorFlow.
- Solid understanding of MLOps principles: model serving, monitoring, drift detection, and retraining pipelines.
- Experience working with cloud infrastructure — AWS preferred — and comfort deploying models in containerised or serverless environments.
- A rigorous, statistically grounded approach to model evaluation — you know when a model is good enough and when it isn't.
- The ability to communicate model behaviour and limitations clearly to non-technical stakeholders.
- Familiarity with data pipeline tooling (e.g. Airflow, dbt, Spark) and an understanding of how ML fits within a broader data platform.
Benefits
Comp & perks- 25 days annual leave + extra days for years of service
- Day off for volunteering & Digital detox day
- Festive Closure - business closed for period between Christmas and New Year
- Cycle to work and electric car schemes
- Free Calm App membership
- Enhanced Parental leave
- Fertility Treatment Financial Support
- Group Income Protection and private medical insurance
- Gym on-site in London
- 7.5% pension contribution by the company
- Discretionary annual bonus up to 10% of base salary
- Talent referral bonus up to £5K
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
Machine LearningModel EvaluationFeature EngineeringModel MonitoringStatistical Analysis
Soft Skills
CommunicationCollaborationProblem-Solving