Machine Learning/AI Engineer â UNSW
Machine Learning/AI Engineer â UNSW
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Job Description
- Salary Level 6: $103866 - $110801 per annum 17% superannuation
- Full time
- Fixed term 12 months
- Location: Kensington Sydney Australia
Summary:
The School of Computer Science and Engineering is one of the largest and most prestigious schools of computing in Australia. It offers undergraduate programmes in Software Engineering Computer Engineering Computer Science and Bioinformatics as well as a number of combined degrees with other disciplines. Our research and teaching staff are world leading and world building as they advance knowledge and learning. For more information on our school go to the following link -
The Machine Learning/AI Engineer will be responsible for the fine-tuning of a Large Language Model (LLM) in university courses within the Faculty of Engineering to provide context aware feedback to students. The Machine Learning/AI Engineer will report to Dr Jake Renzella with nil direct reports.
Skills and Experience:
A degree in a relevant field (e.g. computer science machine learning or artificial intelligence) or an equivalent level of knowledge gained through a combination of education training and/or experience.
Demonstrated ability to work collaboratively in a team environment and effectively communicate with both technical and non-technical stakeholders.
Proven ability to work effectively with minimal supervision including monitoring own workflow and priorities.
Advanced level of computer literacy and programming ability with experience in machine learning frameworks and tools (e.g. TensorFlow PyTorch or scikit-learn).
An understanding of and commitment to UNSWs aims objectives and values in action together with relevant policies and guidelines.
Knowledge of health & safety (psychosocial and physical) responsibilities and commitment to attending relevant health and safety training.
Accountabilities:
Apply an advanced knowledge of machine learning and AI concepts to develop and fine-tuning large language models (LLMs) in support of defined research objectives.
Identify opportunities to improve model performance and contribute to the development of innovative machine learning methodologies.
Undertake testing on new AI models and systems to ensure reliability and alignment with experimental goals.
Liaise with other engineers and stakeholders to finalise AI models and systems for use in experiments.
Develop and maintain high-quality domain-specific datasets to fine-tuning training and validation of the Large Language Model. This includes data cleaning preprocessing and ensuring compliance with data privacy and ethical guidelines.
Prepare comprehensive documentation of AI model development processes and protocols to allow for replication and ensure transparency.
Provide regular progress updates and technical insights to the supervisor regarding progress in AI model development.
Stay abreast of the latest advancements in natural language processing large language models and machine learning techniques. Propose and implement novel approaches to enhance model performance efficiency and the quality of feedback provided to students.
To Apply:
Please click the apply now button and submit your CV Cover Letter and responses to the above Skills and Experience. Please note applications will not be accepted if sent to the contact listed below.
Please note: Visa sponsorship is not available for this position.
Contact:
Applications close: 11:50 pm (Sydney time) on Sunday 21st September 2025
UNSW is committed to evolving a culture that embraces equity and supports a diverse and inclusive community where everyone can participate fairly in a safe and respectful environment. We welcome candidates from all backgrounds and encourage applications from people of diverse gender sexual orientation cultural and linguistic backgrounds Aboriginal and Torres Strait Islander background people with disability and those with caring and family responsibilities. UNSW provides workplace adjustments for people with disability and access to flexible work options for eligible staff. The University reserves the right not to proceed with any appointment.
Employment Type
Full-Time
