Scientific Software Engineer

at  Australian Bureau of Meteorology

• Executive Level 1, Ongoing
• $110,115 - $120,760 + 15.4% super
• All Bureau capital city office locations will be considered
  The Research to Operations (R2O) Program of the Bureau of Meteorology is seeking an experienced software engineer with a computational science background to be a key member of an innovative multi-disciplinary team that improves weather forecast guidance for operational forecast staff. The focus of this team is collaborative development, testing and documentation of weather model Ensemble Post Processing (EPP), including machine learning science, software and systems. Development of EPP products enables greater automation of routine weather forecast production and communication of forecast uncertainty to users.
  Tasks include contributing to development of new “best-guess” and probabilistic high-impact weather forecast algorithms and guidance, utilising outputs from many state-of-the-art numerical weather prediction models. The successful candidate will also contribute to EPP verification and evaluation of algorithm and model outputs, through the specification and development of verification libraries. Processing of large datasets will be required. Work will include collaboration with the UK Met Office on post-processing. Modern tools and practises such as git, automated testing/deployment and code reviews are used. The software used for EPP is written predominantly in C, Python and shell scripts utilising 3rd party scientific and data-handling libraries and is run on Linux platforms. Much of the verification and display software is written in Python.
  Core competencies for the position include mature software engineering skills along with data or computational science knowledge in a scientific setting. Ability to develop complex applications suitable for operational use, and a good grasp of computational algorithms and their application to large data volumes are required. The ability to visualise and interpret gridded scientific fields is desirable. Experience in applying statistical processes to correct errors and machine learning knowledge and experience would be an advantage. A keen interest in systems development based on applied scientific research is desirable.
  This is a great opportunity to work on meaningful scientific software applications for the public good, in an operational environment with a commitment to innovation and quality. Professional development opportunities relevant to the work will be available.

Please refer the Job description for details