Senior Machine Learning Engineer

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

📋 Description

• Enhance current production models through systematic error analysis, improved data quality, targeted experimentation, and modifications to model architecture and training processes.

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

• Collaborate with clinicians and product teams to establish meaningful evaluation criteria, including sensitivity, specificity, and the clinical implications of various error types.

• Assess robustness across diverse patient populations, clinical locations, imaging devices, and acquisition conditions.

• Identify performance deficiencies and provide evidence that improvements are applicable across different contexts.

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

• Construct reproducible training and evaluation pipelines, ensuring traceability of datasets, experiments, and model versions.

• Work alongside software engineers to optimize inference speed, resource utilization, and operational reliability.

• Investigate issues related to models that arise during production.

• Analyze relevant research and evaluate promising methodologies.

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

• Review code and experiments, mentor team members, and clearly communicate findings and trade-offs.

⛳️ Requirements

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

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

• Demonstrated ability to independently navigate complex projects from initial problems to effective solutions.

• Strong foundational knowledge in deep learning and computer vision, with practical experience in image classification, detection, or segmentation.

• Proficient in Python programming.

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

• Proven experience in deploying models into products and assessing performance beyond development datasets.

• Rigorous approach to experimental design and evaluation, including appropriate baselines, uncertainty assessment, failure-mode analysis, and distinguishing significant improvements from noise.

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

• Sound judgment in balancing model quality, complexity, inference costs, and delivery timelines.

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

• Adherence to data privacy, compliance, safety, confidentiality, employment laws, and DeepHealth policies and procedures.

• Preferred: Experience in medical imaging or other applications dealing 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: Experience with distributed training, cloud infrastructure, or inference optimization.

• Preferred: Experience in monitoring deployed models and addressing changes in data or performance over time.

🏝️ Benefits

• Remote work arrangement.

• Occasional travel may be required.