15 hours ago
Responsibilities
- Design, build, maintain, and scale production-grade machine learning systems and pipelines.
- Develop novel machine learning algorithms and models and transition them from experimentation into reliable production products.
- Build ML products using statistical modelling, deep learning, optimisation, and AI techniques across operational, scientific, and R&D domains.
- Translate complex scientific and business problems into well-scoped ML solutions and deployable capabilities.
- Architect and optimise ML systems for production performance, scalability, and reliability.
- Collaborate with data scientists, data engineers, software engineers, and domain experts in cross-disciplinary teams.
- Apply engineering and data science best practices including technical design, design reviews, unit testing, monitoring, alerting, code reviews, and documentation.
- Present technical results, trade-offs, and product outcomes to peers and senior customers.
- Improve 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 in a quantitative field such as Computer Science, Mathematics, Physics, Engineering, or a related discipline.
- Typically 5+ years of experience designing, prototyping, productionising, maintaining, and scaling ML or data science products in complex environments.
- Demonstrable expertise in machine learning algorithms, statistical modelling, and optimisation techniques.
- Applied knowledge of data science and ML tools across 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 such as Python, Go, Java, or C++.
- Advanced SQL knowledge.
- Experience with ML engineering practices including MLOps, model lifecycle management, CI/CD, and monitoring.
- Knowledge of experimental design, analysis, and scientific methodology.
- Strong stakeholder management and ability to influence across teams and organisations.
- Desired qualifications include experience with Hadoop, Hive, or Spark; generative AI, LLMs, or RAG; agentic AI concepts; scientific or R&D workflows; model interpretability; uncertainty quantification; sophisticated experimental methodologies; and ML or AI publications, invention disclosures, or patents.
- A customer-centric, pragmatic, rigorous, detail-oriented, and continuously improving approach is expected.
Benefits
- Competitive compensation and benefits package.
- Opportunity to work on cutting-edge ML and AI problems at global scale.
- Culture focused on scientific rigour, engineering excellence, and continuous learning.
- Hybrid office and remote working arrangement with flexible working options considered.
- Career development pathways in a global technology organisation.
- Up to 10% travel is expected.
- The role is not eligible for relocation assistance.
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