In addition to the exciting developments in generative AI, a parallel revolution in scientific machine learning is unfolding. Since 2022, major progress has been made in the application of neural networks to Earth system modelling. The Bureau is growing its capability to apply data science and machine learning capabilities to a range of scientific challenges in Research. The Data Science and Emerging Technologies Team contributes to research, problem-solving and solutions using new approaches including data science, data engineering, machine learning, visualisation and data analytics. This role is a technically-focused role with a focus on neural network model development including the software development, data pipelines, and training pipelines. As a scientific ML engineer you will lead the technology aspects of machine learning projects in Research. You will join a team of meteorologists, data scientists and software engineers and will employ best practices in your work. You will have the opportunity to collaborate with the rest of the team on research and publications associated with your work. Tasks will focus on machine learning and data science, and will include elements of data engineering (processing of large data volumes in a variety of formats) and software development (versioning code, developing re-usable libraries, participating in code reviews). You will work predominantly in a Linux and Python environment, chiefly on high-performance computing (supercomputing) infrastructure. Is it expected that you will be supported by experienced staff, and given latitude to work independently on agreed tasks. You will also support research activities including publishing results in journals and publishing open source code. The key duties of the position include Lead the development of modern scientific machine learning data pipelines and training frameworks for earth system science, including working with concepts such as automation, repeatable science and big data (i.e. terabytes to petabytes). Contribute to machine learning model development in Earth system science Work effectively in a Linux-based high-performance computing environment (supercomputing) Work effectively across multiple hybrid teams to support machine learning projects. Cultivate and maintain effective working relationships with the Data Science and Emerging Technologies Team, its stakeholders, partners, and customers. Contribute to research and publication. Actively participate in peer code reviews. Comply with all Bureau work, health and safety policies and procedures, and take reasonable care for your own health and safety and that of employees, contractors and visitors who may be affected by your conduct. Be aware of, and apply as necessary, the principles and practices of the various elements of the Bureau's Social Justice Strategy and a demonstrated commitment to the APS Values and Code of Conduct.
