Senior Machine Learning Engineer

This is a fully remote position, open to applicants in Australia.

📋 Description

• Design, enhance, and implement machine learning models for DeepHealth's clinical AI solutions.

• Refine existing production models through thorough error analysis, improved data quality, targeted experimentation, architecture adjustments, and training modifications.

• Create models for new products, starting from initial design and feasibility testing to training, validation, and integration into production.

• Collaborate with clinicians and product team members to establish evaluation metrics, including sensitivity, specificity, and the clinical implications of errors.

• Assess model robustness across diverse patient demographics, clinical environments, imaging technologies, and acquisition conditions.

• Identify performance deficiencies and gather evidence to demonstrate that enhancements are applicable across various contexts.

• Enhance data curation and annotation processes, addressing coverage gaps, label accuracy, and preventing data leakage.

• Develop reproducible training and evaluation frameworks that include traceable datasets, experiments, and model versions.

• Work alongside software engineers to improve inference speed, resource efficiency, and operational dependability.

• Investigate model-related issues that arise during production.

• Analyze relevant literature and test innovative methodologies.

• Assist in validation and technical documentation alongside quality and regulatory teams.

• Review code and experimental work, mentor team members, and communicate findings and trade-offs effectively.

• Adhere to DeepHealth's policies, procedures, privacy regulations, compliance standards, safety protocols, and confidentiality guidelines.

• Fulfill job responsibilities with high quality and punctuality.

⛳️ Requirements

• Bachelor's degree in computer science, engineering, mathematics, or a related field, or equivalent practical experience (mandatory).

• Over 5 years of hands-on experience in developing and deploying machine learning models.

• Proven ability to independently take complex projects from initial concept to a functional solution.

• Strong foundational knowledge in deep learning and computer vision.

• Practical experience in image classification, detection, or segmentation tasks.

• Proficient in Python programming.

• Familiarity with a modern deep learning framework such as PyTorch.

• Demonstrated history of deploying models into production and evaluating performance beyond development datasets.

• Rigor in experimental design and assessment, including establishing suitable baselines, evaluating uncertainty, conducting failure-mode analysis, and discerning significant improvements from noise.

• Strong software engineering principles, including writing maintainable code, testing protocols, version control practices, and ensuring reproducibility.

• Sound judgment regarding trade-offs between model quality, complexity, inference costs, and project timelines.

• Ability to work independently while effectively collaborating across various disciplines, along with clear written communication skills.

• Compliance with all local, regional, and national employment laws.

• Adherence to data privacy, compliance, safety, and confidentiality standards.

• Experience in medical imaging or related applications with variable image quality and limited or noisy labels (preferred).

• Experience in developing and validating models for regulated products (preferred).

• Knowledge of self-supervised learning, transfer learning, or foundational models in computer vision (preferred).

• Familiarity with distributed training, cloud infrastructure, or inference optimization (preferred).

• Experience in monitoring deployed models and addressing shifts in data or performance over time (preferred).

🏝️ Benefits

• Occasional travel may be necessary.

• Flexible remote or hybrid working arrangements.