Senior Machine Learning Engineer

About the role

We are looking for a Senior Machine Learning Engineer who combines deep machine learning expertise with strong software engineering discipline to design, build, and deploy production-grade ML and AI systems. This role goes beyond traditional ML engineering. You will apply machine learning science as a core discipline — developing novel algorithms and models that are not only experimentally validated but architected and deployed as scalable, reliable products.

Responsibilities

• Design, build, and maintain scalable, production-grade machine learning systems and pipelines using modern engineering practices (CI/CD, testing, monitoring, observability).
• Apply machine learning science to develop novel algorithms and models that are deployed as reliable, scalable products — not limited to experimentation but extending through to production delivery and operational use.
• Leverage statistical modelling, deep learning, and AI techniques across operational, scientific, and R&D domains to deliver impactful ML products.
• Architect and optimise ML systems for performance, scalability, and reliability in production environments.
• Collaborate closely with data scientists, data engineers, software engineers, and domain experts as part of cross-disciplinary teams.
• Adhere to and advocate for engineering and data science guidelines (technical design, design reviews, unit testing, monitoring & alerting, code reviews, documentation).
• Present technical results, trade-offs, and product outcomes to peers and senior interested parties.
• Contribute to improving developer velocity, engineering standards, and shared tooling.
• Mentor junior team members and contribute to the technical growth of the wider team.

Requirements

• MSc or PhD degree or equivalent experience in a quantitative field (e.g. Computer Science, Mathematics, Physics, Engineering, or related discipline).
• Hands-on experience (typically 5+ years) designing, prototyping, productionizing, maintaining, and scaling ML/data science products in sophisticated environments.
• Strong and demonstrable expertise in machine learning algorithms, statistical modelling, and optimisation techniques — with a track record of applying these to build production-grade solutions.
• Applied knowledge of data science and ML tools across all stages of the data and model lifecycle.
• Thorough understanding of the mathematical foundations of statistics, machine learning, and scientific computing.
• Strong programming experience in one or more object-oriented languages (e.g. Python, Go, Java, C++).
• Advanced SQL knowledge.
• Experience with modern ML engineering practices including MLOps, model lifecycle management, CI/CD, and monitoring.
• Knowledge of experimental design, analysis, and scientific methodology.
• Customer-centric and pragmatic mentality with a focus on value delivery and swift execution, while maintaining rigour and attention to detail.
• Strong stakeholder management and ability to influence across teams and organisations.
• Continuous learning and improvement mindset.

Qualifications

• Essential: MSc or PhD degree or equivalent experience in a quantitative field.
• Desired: Experience with big data technologies (e.g. Hadoop, Hive, Spark); Experience with generative AI, LLMs, or retrieval-augmented generation (RAG); Exposure to Agentic AI concepts, including autonomous agents, tool use, and orchestration frameworks; Experience applying machine learning and AI to scientific or R&D workflows; Familiarity with model interpretability, uncertainty quantification, and advanced experimental methodologies; Proven record of publications, invention disclosures (IDFs), or patents in machine learning or AI.

Benefits

Competitive compensation and benefits package.
Opportunity to work on cutting-edge ML and AI problems at global scale.
Culture that values scientific rigour, engineering excellence, and continuous learning.
Broad career development pathways in a world-class technology organisation.

Schedule

Hybrid working arrangements and a commitment to work-life balance.

Skills

• Cloud Platforms
• Collaboration
• Communication
• Configuration management and release
• Continuous deployment and release
• Creating a high performing team
• Database Design
• Digital Project Management
• Documentation and knowledge sharing
• Emerging technology monitoring
• Facilitation
• Information Security
• Mentoring
• Metrics definition and instrumentation
• NoSql data modelling
• Problem Solving
• Relational Data Modelling
• Risk Management
• Scripting
• Secure development
• Service operations and resiliency
• Software Design and Development
• Solution Architecture
• Source control and code management