Machine Learning Engineer, Valuations

at  Motorway

ABOUT MOTORWAY

Motorway is the UK’s fastest-growing used car marketplace – our award winning, online-only platform connects private car sellers with thousands of verified dealers nationwide, ensuring everyone gets the best deal. Founded in 2017, our technology-led approach has redefined the experience of selling a car, generating thousands of monthly car sales and helping us to grow to a team of over 400 across 4 locations.
We are now valued at over $1 billion and is backed by some of the world’s leading technology investors, having raised £143 million in Series C funding. This is a unique opportunity to join a fast-growing scale-up at a crucial phase of growth and help change an industry for the better.

REQUIREMENTS

• Proven experience deploying ML models at scale, with a good understanding of the requirements of state-of-the-art regression and probabilistic models (e.g. XGBoost, GPs, Bayesian Models).
• Strong production proficiency in Python (e.g., pandas, scikit-learn, fastAPI/flask, pydantic, DVC) and SQL.
• Strong knowledge of systems design, including serverless and event-driven microservice architecture.
• Demonstrable passion for engineering excellence, with strong practices within the context of ML including OOP, abstraction, error handling, and logging.
• Ability to troubleshoot and optimise production systems for reliability and scalability.
• Strong understanding of ML lifecycle tools like Kubeflow and familiarity with cloud platforms (GCP, Vertex AI, AWS).
• Experience with CI/CD pipelines (e.g. GitHub Actions), Docker, and IaC tools (e.g., Terraform).
• Experience building ML evaluation frameworks and writing tests (unit, integration).
• A strong sense of ownership, autonomy, and a highly organised nature.
• Proficiency in Typescript/JavaScript and Node.js is a plus.

ABOUT THE ROLE

At Motorway, technology is at the heart of what we do, and as a result we are building a world-class data science organisation with a mission to enable the highest quality human and machine decision making. We’re placing data professionals throughout the company - in product, business and platform teams so that we can push the boundaries of vehicle profiling and valuation automation for our car sellers and dealers. We move fast; we’re always looking for new ideas and we’re very transparent about the decisions we make and why we make them.
We’re seeking a Machine Learning Engineer to join our Vehicle Valuations Team and contribute to the evolution of our vehicle pricing algorithms and cloud ML platform infrastructure. This role sits at the intersection of machine learning and software engineering, and we’re looking for talented and enthusiastic people to deliver scalable, robust systems that power millions of accurate vehicle valuations a year, supporting Motorway’s mission to redefine the used car marketplace.
You’ll also bring strong backend engineering capabilities to the team, helping to build and maintain production systems, with a focus on systems design and seamless integration. Your work will have a direct impact on our core offering by ensuring we can serve thousands of customers daily.

KEY RESPONSIBILITIES

• Develop and deploy machine learning models: Design, build, and deploy robust, scalable machine learning models (with a focus on regression and probabilistic approaches) to enhance our vehicle pricing service. This includes managing the entire ML lifecycle, from experimentation and training to deployment, monitoring, and retraining, leveraging Google Cloud Platform (GCP) and Vertex AI.
• Build and maintain production ML systems: Develop and maintain high-performance backend systems and APIs that reliably deliver real-time pricing predictions across multiple platforms. Ensure these systems are optimised for efficiency, scalability, and stability.
• Collaborate effectively: Work closely with a cross-functional team of data scientists, analysts, and engineers to identify and solve challenging pricing problems. Partner with our central Infrastructure team to align with and contribute to company-wide cloud infrastructure best practices and standards.
• Drive innovation: Proactively participate in technical decision-making, shaping the future of our pricing architecture. Stay current and curious about the application of emerging technologies (including generative AI) and contribute innovative ideas to enhance our ML capabilities.