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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.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and deploying machine learning models in production, with strong proficiency in Python and familiarity with MLOps principles. Capable of collaborating with cross-functional teams to translate complex model outputs into user-friendly product features.
Highest-signal resume keywords
Machine Learning Model DeploymentPython ProgrammingMLOps PrinciplesCloud Infrastructure (AWS)Data Pipeline Tooling (Airflow, dbt, Spark)
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningModel EvaluationFeature EngineeringModel MonitoringStatistical Analysis
Soft Skills
CommunicationCollaborationProblem-Solving
Tools & Technologies
Scikit-learnPyTorchTensorFlowAWSContainerization
Industry Keywords
Data QualityExperiment TrackingModel VersioningData IngestionUK Property Transactions
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