Staff Machine Learning Engineer

Entity: Technology Job Family Group: IT&S Group Job Description: We are seeking an exceptional Staff Machine Learning Engineer to serve as a technical leader and architect of advanced machine learning systems across the organisation. This is the highest individual contributor tier within the field — a role for engineers and scientists who not only build world-class ML systems but define how they are built. You will shape architectural direction, establish engineering and scientific standards, and drive the delivery of complex, high-impact ML products that span the full journey from scientific research and experimentation through to scalable, production-deployed solutions. A core differentiator of this role is deep applied machine learning science: the ability to develop, validate, and deploy novel ML algorithms and scientific models as reliable, maintainable products — bridging the gap between cutting-edge research and enterprise-scale deployment. You will influence multiple teams, mentor senior engineers, and drive step-change impact across business-critical, scientific, and R&D domains. Key Responsibilities Provide technical leadership in the design and architecture of large-scale, production-grade ML systems and platforms across the organisation. Own end-to-end delivery of complex ML solutions — from scientific problem framing and algorithm design through to deployment, operationalisation, and product delivery. Apply advanced machine learning science to develop novel algorithms and models, ensuring they are rigorously validated and deployed as scalable, reliable, production-grade products. Bridge the gap between scientific research and enterprise deployment — taking ML innovations from experimentation through to productised, maintainable solutions that deliver measurable value. Drive engineering excellence across ML systems, including CI/CD, testing, observability, reliability, and MLOps best practices. Define technical standards, patterns, and best practices for ML engineering and applied ML science across teams. Lead complex, multi-team technical initiatives and influence organisational direction through technical authority. Evaluate and integrate emerging approaches — including generative AI, Agentic AI, advanced optimisation, and scientific computing — into scalable solutions. Contribute to and shape internal ML platforms, reusable frameworks, and shared scientific computing capabilities. Mentor senior engineers and data scientists, raising the technical bar across the discipline. Partner with business and scientific customers to shape ML strategy and identify high-value opportunities. Present technical strategies, architectural decisions, and outcomes to senior leadership. Qualifications Essential MSc or PhD degree in a quantitative field (e.g. Computer Science, Mathematics, Physics, Engineering, or related discipline). Hands-on experience (typically 8+ years) designing, prototyping, productionising, and scaling complex ML systems in production environments. Deep and demonstrable expertise in machine learning algorithms, statistical modelling, optimisation techniques, and scientific computing — with a proven track record of applying these to deliver production-grade products. Strong software engineering and system design expertise, including distributed systems, scalable architectures, and API design. Advanced programming experience in languages such as Python, Go, Java, or C++. Advanced SQL knowledge. Strong experience with MLOps, production ML systems, model lifecycle management, and monitoring. Experience working with large-scale data systems and distributed computing frameworks (e.g. Spark, Hadoop). Knowledge of experimental design, scientific methodology, and analysis. Strong customer management and proven ability to influence large organisations without direct authority. Demonstrated ability to lead through technical excellence and deliver high-impact, organisation-wide outcomes. Continuous learning and improvement approach. Desired Deep experience in applied machine learning science — including developing novel algorithms and translating scientific research into deployable, production-grade ML products. Experience applying AI/ML to scientific, engineering, or R&D workflows — encompassing experimentation, simulation, optimisation, physics-informed modelling, and autonomous scientific workflows. Strong experience with generative AI (LLMs, RAG, multimodal systems) and their deployment in production. Experience designing or deploying Agentic AI systems — including autonomous agents, tool use, multi-agent orchestration, reasoning workflows, and agent-driven scientific discovery. Proven track record of innovation through publications in peer-reviewed venues, invention disclosures (IDFs), patents, or open-source contributions in machine learning or AI. Experience building ML platforms, reusable scientific computing frameworks, or internal tooling that accelerates delivery across teams. Familiarity w