Senior Machine Learning Engineer

Jobgether; City of Melbourne; Hybrid; AUD 140,000 - 190,000; Full time

Benefits offered by this job

Remote/hybrid work environment

Job summary

Jobgether, on behalf of a partner company, seeks a Senior Machine Learning Engineer based in Australia to join an applied ML environment focused on clinical products. You will work across the full ML lifecycle, from data and experimentation through model development, evaluation, and production deployment.

The role combines deep learning and computer vision with strong software engineering and experimental rigor, improving production models and developing new solutions from problem formulation

Qualifications

• Bachelor's degree or equivalent practical experience.
• 5+ years of hands-on ML model development and delivery.
• Strong foundations in deep learning and computer vision (image classification, object detection, segmentation).
• Advanced Python and PyTorch experience.
• Proven experience deploying ML models into production and measuring performance.
• Strong experimental design and evaluation skills (baselines, uncertainty, failure modes).
• Sound software engineering practices (maintainable code, tests, version control, documentation).
• Ability to balance model quality, cost, and delivery timelines.
• Excellent written and verbal communication; autonomous with team collaboration.
• Experience in medical imaging or regulated products is highly desirable.
• Knowledge of self-supervised learning, transfer learning, or foundation models is a plus.
• Experience with distributed training, cloud infrastructure, or inference optimization is beneficial.
• Experience monitoring models and adapting to data drift is desirable.

Responsibilities

• Improve production ML models through data, experiments, and architecture changes.
• Develop ML models for new products from formulation to training, validation and deployment.
• Collaborate with clinicians and product stakeholders to define evaluation criteria.
• Evaluate robustness across patient populations, sites, equipment, and conditions.
• Improve data curation and reduce data leakage.
• Build reproducible training/evaluation pipelines with traceable data and model versions.
• Work with software engineers to optimize inference performance and reliability.
• Research and test new techniques, guiding technology choices.
• Contribute to model validation and documentation with quality/regulatory teams.
• Review code, provide feedback, and mentor colleagues.
• Ensure compliance with data privacy, safety, and regulatory standards.

Skills

Deep learning

Computer vision

Python

Model deployment

Experiment design

Software engineering

Communication

Multidisciplinary collaboration

Education

Bachelor's degree in CS/Engineering/Math

Tools

PyTorch

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Machine Learning Engineer based in Australia.

Join an applied machine learning environment focused on developing AI solutions that deliver meaningful improvements in clinical products.
You’ll work across the full ML lifecycle, from data and experimentation through model development, evaluation, and production deployment.
The role combines deep learning and computer vision expertise with strong software engineering and experimental rigor.
You’ll improve production models while also developing new solutions from initial problem formulation through validation and integration.
Working closely with machine learning engineers, software engineers, clinicians, and product teams, you’ll translate complex problems into measurable technical outcomes.
A major focus will be ensuring models are robust across diverse patient populations, clinical environments, imaging equipment, and acquisition conditions.
This is a hands-on opportunity to contribute to high-impact AI products while shaping reliable, reproducible, and production-ready machine learning systems.

Accountabilities:

• Improve existing production machine learning models through systematic error analysis, improved data, targeted experimentation, and changes to model architectures and training approaches.
• Develop machine learning models for new products, taking problems from initial formulation and feasibility experiments through training, validation, and production integration.
• Collaborate with clinicians and product stakeholders to define meaningful evaluation criteria, including sensitivity, specificity, and the clinical implications of different error types.
• Evaluate model robustness across patient populations, clinical sites, imaging equipment, and acquisition conditions, identifying performance gaps and generating evidence that improvements generalize effectively.
• Improve data curation and annotation workflows by addressing coverage gaps, label quality, and potential sources of data leakage.
• Build reproducible training and evaluation pipelines with traceable datasets, experiments, and model versions.
• Partner with software engineers to optimize inference performance, resource consumption, and operational reliability, while investigating model issues that arise in production.
• Research relevant scientific developments, test promising approaches, and make evidence-based decisions about technologies and methodologies to adopt.
• Contribute to model validation and technical documentation in collaboration with quality and regulatory teams.
• Review code and experiments, provide constructive technical feedback, mentor colleagues, and communicate technical findings, risks, and trade-offs clearly.
• Follow applicable data privacy, compliance, safety, confidentiality, quality, and regulatory standards throughout the development and delivery lifecycle.
• Maintain a professional, collaborative, and accountable approach while adapting to new technologies, methods, systems, and responsibilities.

Requirements:

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related discipline, or equivalent practical experience.
• 5+ years of hands-on experience developing and delivering machine learning models, with demonstrated ability to independently take complex problems from initial formulation through to working solutions.
• Strong foundations in deep learning and computer vision, including practical experience with image classification, object detection, or image segmentation.
• Advanced Python skills and experience with a modern deep learning framework such as PyTorch.
• Proven experience deploying machine learning models into production products and measuring their performance beyond development datasets.
• Strong experimental design and evaluation skills, including appropriate baselines, uncertainty analysis, failure-mode analysis, and the ability to distinguish meaningful improvements from statistical or experimental noise.
• Strong software engineering practices, including maintainable code, testing, version control, documentation, and reproducibility.
• Sound technical judgment when balancing model quality, complexity, inference costs, resource requirements, and delivery timelines.
• Ability to work autonomously while collaborating effectively with multidisciplinary teams, with strong written and verbal communication skills.
• Experience with medical imaging or other applications involving variable image quality and limited or noisy labels is highly desirable.
• Experience developing and validating models for regulated products is an advantage.
• Knowledge of self-supervised learning, transfer learning, or foundation models for computer vision is a plus.
• Experience with distributed training, cloud infrastructure, or inference optimization is beneficial.
• Experience monitoring deployed models and addressing changes in data distributions or model performance over time is desirable.
• Strong professionalism, integrity, confidentiality, adaptability, and commitment to quality and timely delivery.

Benefits:

• Remote/hybrid working environment.
• Position based in Melbourne, Victoria, Australia.
• Opportunity to work on applied machine learning and computer vision solutions with meaningful clinical applications.
• End-to-end exposure across data, experimentation, model development, validation, deployment, and production monitoring.
• Collaboration with machine learning engineers, software engineers, clinicians, product teams, and quality and regulatory specialists.
• Opportunity to work on challenging problems involving model robustness, clinical variability, computer vision, and production AI.
• Scope to influence technical decisions around model architecture, experimentation, inference optimization, and engineering practices.
• Opportunities to mentor colleagues and contribute to the evolution of machine learning development standards and workflows.
• The source posting does not specify a salary range or additional formal benefits.