AI / Machine Learning Engineer
Help develop and improve production-grade ML systems operating on real-world data in edge environments. Practical machine-learning engineering - maintaining and improving deployed models, analysing datasets, tuning performance, and ensuring models perform reliably outside controlled environments.
About the role
A Sydney-based engineering company building production-grade AI systems for real-world deployment. The team works across AI/ML, software, data and systems engineering - fast-moving, deeply technical, with strong collaboration across disciplines.
This is practical machine-learning engineering, not isolated model training. You will be operating on imperfect real-world data, deploying to edge environments, and partnering with software engineers to get models into production and keep them there.
Why AEY is running this
Confidential client - a Sydney-based engineering firm AEY partners with on multiple technical mandates. Full company details shared under NDA on a confidential call.
Process
Two technical stages plus a final conversation with engineering leadership. AEY manages scheduling, preparation and debriefs throughout.
Responsibilities
- ▸ Develop, refine and optimise deep-learning models and inference pipelines.
- ▸ Evaluate model performance using real-world datasets and operational test cases.
- ▸ Improve model reliability, accuracy and deployment readiness.
- ▸ Monitor production model performance and troubleshoot issues.
- ▸ Work with datasets - analysis, preprocessing, labelling and enrichment.
- ▸ Collaborate with software engineers to integrate ML models into production systems.
- ▸ Write clean, maintainable, production-quality Python code.
- ▸ Contribute across the full ML lifecycle - from data collection through to deployment and monitoring.
Requirements
- ▸ 2+ years' experience in machine-learning or computer-vision roles.
- ▸ Strong practical understanding of deep-learning workflows and model optimisation.
- ▸ Confidence working with real-world datasets and imperfect data.
- ▸ Python, PyTorch, Pandas; comfort in Linux environments.
- ▸ Familiarity with modern computer-vision architectures (YOLO, ResNet, U-Net).
- ▸ Strong software-engineering habits and clean coding practices.
Bonus
- ▸ Academic or research experience in deep learning or computer vision.
- ▸ Edge deployment or production inference systems.
- ▸ Exposure to signal-processing or RF-related datasets.
- ▸ Production monitoring and ML performance analysis.
Tell us what you are looking for.
If this role isn't the one, we likely have something adjacent - or coming up. A short conversation is enough to align.