Senior Machine Learning Engineer in London

We work in small, fully autonomous teams that have real ownership of their products. We use the best tool for the job and constantly look for better. We are seeking a production-focused Machine Learning Engineer to bridge the gap between data science research and scalable, reliable software.

In this role, you will partner with Data Scientists to re-architect experimental models (POCs)—such as Next Best Action and Churn Propensity—for production. You will own "Day 2" operations including deployment, latency optimization, and monitoring, while also building the infrastructure for GenAI and RAG applications powering our tools.

As a Machine Learning Engineer at UW, your responsibilities will include:

• Predictive Modelling: Design and deploy robust ML models to solve business challenges, specifically Churn Propensity and Next Best Action (NBA) engines.
• Customer Analytics: Develop advanced Customer Segmentation using clustering techniques to tailor services and communications.
• Commercial Valuation: Own xLTV and ROI logic, modeling long-term customer value to optimize acquisition and retention spend.
• Deployment & Ops: Collaborate with Data Engineers to productionise scalable models, ensuring continuous monitoring for drift and performance.
• Experimentation: Design and analyse A/B tests to validate model effectiveness and measure commercial uplift.
• Stakeholder Partnership: Translate complex statistical outputs into actionable insights for Marketing, Product, Commercial and Ops stakeholders.

Qualifications

We put people first. It’s all about you.

• Technical Mastery:
• Production ML Experience: Proven experience deploying Machine Learning models into high-traffic production environments (retail, fintech, or utilities experience is a plus).
• Tech Stack: Strong proficiency in Python and software engineering best practices (unit testing, modular code, Git). Experience with containerization (Docker, Kubernetes) is essential.
• MLOps Tooling: Experience with model registries and monitoring tools (e.g., MLflow, Grafana).
• Desirables: Experience with Feature Stores (e.g., Feast, Tecton). Knowledge of streaming data technologies (Kafka, Pyspark). Hands-on experience building or deploying LLM-based applications, specifically working with RAG architectures and vector databases.

Impact & Scope:

You have a track record of leading high-impact initiatives that align with company strategy. You can evaluate proposed work against team goals and provide critical feedback to ensure value delivery.

Planning & Delivery:

You are capable of independently implementing small to medium sized features through to completion.

Operational Excellence:

Continuous improvement mindset: Identify process gaps and proactively propose solutions, seeking out feedback from your team.

Business & Domain Knowledge:

Experience in working in a relevant consumer-centric domain. Can advise stakeholders on how Machine Learning Engineering can be applied to solve business problems.

Leadership & Culture:

• Collaboration: A "Software Engineering mindset" with the ability to work empathetically with Data Scientists, understanding their workflows while enforcing production standards.
• Strategic Problem Solving: Ability to break down vague, high-level business requirements into concrete, scalable technical architectures.
• Clear Communication: Excellent verbal and written skills, with the ability to influence technical and non-technical audiences.
• Accountability: Willingness to take ownership of critical systems and participate in on-call rotations.
• Continuous Learning: Proactively seeking out the latest industry trends and introducing relevant innovations to the team.

Don’t worry if you don’t have the whole list. If you feel you have most of it and can learn the rest pretty quickly then please don’t hesitate to apply. Overall we are looking for imaginative and pragmatic problem-solvers who want to help make a positive impact with data at UW.

Please note we cannot offer visa sponsorship now or in the future to work at UW.

Additional Information

• Competitive salary: We benchmark against the industry and will share the salary openly during our first conversation.
• Performance bonus: An annual discretionary bonus ranging from 15-40%.
• Work-life balance: We offer an optional four-day working week (90% pay for 90% impact).
• Work from anywhere: You can work abroad for up to three weeks, twice every tax year.
• Holiday: 25 days plus bank holidays (increasing with tenure), with the option to trade up to five days each year.
• UW discounts: Save on our services and you’ll also get access to 100s of rewards and discounts through Perkbox.
• Future planning: Matched-contribution pension scheme and life assurance (up to 4x salary).
• Family first: Policies designed to help you and your family thrive.
• Flexible benefits: An allowance for private health insurance, dental insurance, or gym membership.
• Sabbaticals: An eight-week paid sabbatical after four years of service.
• Growth: A dedicated learning and development budget and bi-annual promotion cycles.
• Inclusion: Join belonging groups that help shape our culture.
• Events: Company-wide celebrations including the ‘Great Big Get Together’ and our ‘Good Hearted Go-Getter Awards’.

We provide equal opportunities, a diverse and inclusive work environment, and fairness for everyone. You are welcome to apply no matter your age, disability, gender, marriage or civil partnership status, pregnancy and maternity status, race, religion or belief, or sexual orientation. Please don’t be afraid to ask about what we can do to support your needs. All requests will be carefully and fairly considered.

Please note, if you are successful and offered a role at UW, you will be subject to a background check. Where checks are unsatisfactory or incomplete and/or a failure to reveal information relating to convictions that you are required to identify as part of the background checks, could lead to withdrawal of an offer of employment.