Senior Machine Learning Engineer
The role focuses on turning proof‑of‑concept models into production‑ready pipelines and live APIs, overseeing deployment, monitoring, and automated retraining. Collaboration spans data scientists, software engineers, product managers, and stakeholders to embed ML solutions into Hometrack's property valuation services.
Skills required
About this role
Hometrack is redefining the mortgage journey for lenders, brokers, and consumers by delivering market-leading valuation and property data services to the financial, property, and technology industries. Our key commercial and go-to-market segment is in financial services, primarily mortgage lenders, including nine of the top 10 mortgage providers.
At Hometrack, we are looking for an experienced Senior Machine Learning Engineer, to translate POC model code from data science and analytics into robust pipelines and live APIs.
Responsibilities:
- Oversee the deployment of machine learning models into production, ensuring they perform well in real-world conditions and monitoring their performance over time.
- Work closely with data scientists, software engineers, product managers, and stakeholders to integrate machine learning solutions into our products and services.
- Develop automated workflows for model retraining, testing, and deployment to streamline the machine learning lifecycle.
- Advocate for and implement best practices in software engineering, including code reviews, continuous integration, and continuous delivery (CI/CD) for machine learning models.
Requirements:
- Strong understanding of machine learning applications, development life cycle processes and tools: CI/CD, version control, testing frameworks, MLOps.
- Strong Python experience and knowledge, with the ability to write stable, scalable and maintainable code.
- Experience with data science Python libraries such as Sckit-learn, Pandas, NumPy, PyTorch, PySpark, LightGBM.
- Have worked with a cloud service, such as AWS.
- Familiarity developing Infrastructure as code (e.g Terraform, cloudformation).
- Have some experience with data engineering, building data pipelines with PySpark, SQL (E.g in Databricks, Glue) to power machine learning applications.
- Comfortable working with Docker and containerised applications.
- Experience leveraging AI native engineering tooling.
- Passion for building products that meet customer needs and business objectives.
- Strong sense of responsibility and a track record of delivering high-quality results in a fast-paced environment.
Benefits
- Everyday Flex - greater flexibility over where and when you work
- 25 days annual leave + extra days for years of service
- Day off for your birthday, house move, good deed day, and digital detox day
- Cycle to work and electric car schemes
- Free Calm App membership
- Enhanced Paternity Leave
- Fertility Treatment Financial Support
- Group Income Protection and private medical insurance
- Gym on-site in London – or membership in regional offices
- 7.5% pension contribution by the company
- Discretionary annual bonus up to 10% of base salary
- Talent referral bonus up to £5K
