Senior ML Engineer

Accountabilities:: You will lead the development and productionization of machine learning solutions, collaborating with engineering, product, and data teams to deliver reliable, measurable improvements across payment workflows. Own the machine learning lifecycle: Research, test, train, deploy, and monitor ML-powered features, taking responsibility for their performance and reliability in production. Optimize payment routing: Develop and productionize intelligent routing decisions across payment flows, building the infrastructure required to support scalable and dependable ML solutions. Build production-ready ML systems: Establish and improve model versioning, CI/CD workflows, observability, monitoring, and other MLOps practices needed for reliable deployment and maintenance. Collaborate across teams: Work closely with product managers, data specialists, and software engineers to identify valuable use cases and translate them into shipped features with measurable outcomes. Design and evaluate experiments: Apply statistical reasoning, experimentation, and A/B testing to validate hypotheses, assess model performance, and guide data-driven decisions. Prioritize practical impact: Evaluate alternative approaches pragmatically, balancing model sophistication, implementation complexity, business value, and operational reliability. Mentor engineering colleagues: Share ML knowledge, explain technical trade-offs, and help other engineers adopt machine learning techniques effectively. Engage with customers: Speak directly with customers to validate ideas, understand real-world requirements, and ensure solutions address practical needs. Shape technical direction: Help prioritize promising use cases, influence engineering decisions, and establish repeatable patterns and practices for future ML initiatives. Requirements: You are an experienced machine learning professional with strong software engineering foundations, statistical thinking, and the ability to independently bring models into production. You are comfortable working in an evolving environment where priorities and processes are still taking shape. Relevant experience: Senior-level experience in machine learning engineering, data science, or applied research, with a demonstrated track record of deploying models into production through APIs, batch processing, or streaming systems. Python proficiency: Strong Python programming skills and hands-on experience with ML libraries such as scikit-learn, XGBoost, TensorFlow, PyTorch, or Keras. Statistics and experimentation: Ability to design and run experiments and A/B tests, interpret statistical results, and communicate clearly what the evidence does and does not demonstrate. Software engineering expertise: Solid understanding of modern software engineering practices, infrastructure, and data tooling, including the operational challenges associated with the full machine learning lifecycle. MLOps knowledge: Familiarity with the requirements of production ML systems, including deployment, monitoring, model versioning, and continuous integration and delivery. Reinforcement learning fundamentals: Understanding of reinforcement learning concepts and their applications, including multi-armed and contextual bandits. Previous production experience is not required. Cloud experience: Experience with cloud platforms, preferably AWS, although GCP or Azure experience is also relevant. Independent execution: Ability to research, develop, deploy, and maintain solutions independently while making sound technical decisions in ambiguous situations. Collaboration and communication: Strong ability to work with cross-functional teams, engage with customers, explain technical trade-offs, and contribute to shared technical direction. Payments or e-commerce exposure: Experience in payments or e-commerce is an advantage but is not required, as domain knowledge can be developed on the job. Growth mindset: Willingness to help establish practices, mentor colleagues, and build a machine learning function while its priorities and processes continue to evolve. Benefits: Fully remote work environment with a globally distributed team. Competitive share options. Flexible annual leave, with a minimum of 25 days to be taken. Access to coworking spaces across major cities. Company workations and an annual company retreat. High-quality equipment suited to your role. £500 contribution toward your home office setup. Generous learning and professional development budget. Private medical insurance. Additional location-dependent perks and benefits. Supportive onboarding and a collaborative environment that encourages technical ownership, experimentation, and professional growth. An inclusive workplace that values diverse backgrounds and encourages candidates to apply even if they do not meet every listed requirement.