15 hours ago
Responsibilities
- Architect large-scale, production-grade machine learning systems and platforms across the organisation.
- Own end-to-end delivery of ML solutions from scientific problem framing and algorithm design through deployment, operationalisation, and product delivery.
- Develop, validate, and deploy novel machine learning algorithms and scientific models as scalable, reliable products.
- Bridge scientific research and experimentation with maintainable enterprise production solutions.
- Drive engineering excellence across ML systems, including testing, observability, reliability, and MLOps practices.
- Define technical standards, patterns, and best practices for ML engineering and applied ML science.
- Lead complex, multi-team technical initiatives and influence organisational direction through technical authority.
- Evaluate and integrate generative AI, Agentic AI, optimisation, and scientific computing approaches.
- Contribute to internal ML platforms, reusable frameworks, and shared scientific computing capabilities.
- Mentor senior engineers and data scientists.
- 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.
Requirements
- MSc or PhD in a quantitative field such as Computer Science, Mathematics, Physics, Engineering, or a related discipline.
- Typically 8+ years of hands-on experience designing, prototyping, productionising, and scaling complex ML systems in production environments.
- Deep expertise in machine learning algorithms, statistical modelling, optimisation techniques, and scientific computing, with experience delivering production-grade products.
- Strong software engineering and system design expertise, including distributed systems, scalable architectures, and API design.
- Advanced programming experience in Python, Go, Java, or C++.
- Advanced SQL knowledge.
- Strong experience with MLOps, production ML systems, model lifecycle management, and monitoring.
- Experience with large-scale data systems and distributed computing frameworks such as Spark or Hadoop.
- Knowledge of experimental design, scientific methodology, and analysis.
- Strong customer management skills and the ability to influence large organisations without direct authority.
- Demonstrated ability to lead through technical excellence and deliver organisation-wide outcomes.
- Desired experience includes applied ML science, scientific or engineering workflows, simulation, optimisation, physics-informed modelling, and autonomous scientific workflows.
- Desired experience with generative AI, LLMs, RAG, multimodal systems, and production deployment.
- Desired experience designing or deploying Agentic AI systems involving autonomous agents, tool use, multi-agent orchestration, reasoning workflows, or scientific discovery.
- Innovation demonstrated through peer-reviewed publications, invention disclosures, patents, or open-source contributions is desirable.
- Experience building ML platforms, reusable scientific computing frameworks, or internal tooling is desirable.
- Familiarity with model interpretability, uncertainty quantification, and advanced experimental frameworks is desirable.
- No prior energy industry experience is required.
Benefits
- Competitive compensation and benefits package.
- Hybrid office/remote working arrangement with flexible working options and a commitment to work-life balance.
- Career development pathways, mentoring, and opportunities to shape ML and AI at a large technology organisation.
- Up to 10% travel is expected.
- Relocation assistance is available within the country.
- Full-time employment; flexible working arrangements may be considered.
- Access to collaboration spaces, Business Resource Groups, and diversity, equity, and inclusion programs.
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