Machine Learning Engineer

At Resmed, we’re changing lives with every breath. As a Machine Learning Engineer on our AI Engineering team, you’ll help turn data and models into reliable production capabilities that improve patient, customer and business outcomes.

You’ll work across the ML lifecycle, building pipelines, deploying and monitoring models, and developing services that bring AI into real-world applications. This is an opportunity to combine machine learning, Python, software engineering and cloud technologies while taking ownership of well-defined components from implementation through production support.

• →Design, build, test and operate production ML components and services within a cross-functional AI/ML environment.
• →Build reliable pipelines spanning data preparation, feature engineering, model training, evaluation, deployment and monitoring.
• →Develop APIs and services that integrate machine learning capabilities into web, mobile and backend applications.
• →Write maintainable, tested Python code using strong engineering practices across version control, code review, automated testing and debugging.
• →Train, evaluate, deploy, manage and retrain ML models, applying appropriate machine learning and statistical techniques.
• →Monitor production ML systems and troubleshoot data quality, model performance, reliability, scalability and cost.
• →Partner with Data Scientists, Machine Learning Engineers, Data Engineers, Software Engineers and Product partners to move prototypes and requirements into production.
• →Evaluate technical approaches and clearly communicate implementation choices, dependencies and trade-offs.
• →Own well-defined components or technical workstreams through implementation and production support, while contributing to engineering standards, documentation and continuous improvement.
• →Share knowledge across the team and support less-experienced colleagues where appropriate.

• Bachelor’s degree in Computer Science, Engineering, Statistics, Mathematics or a related technical field, or equivalent practical experience.
• 1–2 years of professional experience in Machine Learning Engineering, Data Engineering, Software Engineering or a similar role, including production ML experience or equivalent demonstrated experience.
• Working knowledge of the production ML lifecycle, including data and feature pipelines, training, evaluation, inference, APIs, deployment, continuous training and monitoring.
• Strong Python and software-engineering fundamentals, including experience with pandas, NumPy and scikit-learn.
• Experience with relational SQL databases and at least one NoSQL or distributed data technology.
• Hands-on experience training, evaluating and serving ML models through APIs or batch-processing services.
• Experience with workflow orchestration such as Airflow, a major cloud platform such as AWS, Docker and Git-based CI/CD.
• Ability to monitor and troubleshoot production services, pipelines and model behaviour.
• Clear written and verbal communication across technical and non-technical audiences.

Nice to have

• Experience with MLOps platforms such as Amazon SageMaker or MLflow, Kubernetes or Amazon EKS, Snowflake, Kafka or Flink.
• Familiarity with model explainability, responsible AI, bias or fairness assessment.
• Familiarity with large language models, retrieval-augmented generation, LangChain or LangGraph, or experience in healthcare, medical devices or privacy-sensitive data environments.

• Ownership: You take responsibility for defined technical work from implementation through production support.
• Practical judgement: You weigh dependencies and trade-offs and make thoughtful implementation choices.
• Collaboration: You work openly across disciplines to turn requirements and prototypes into dependable solutions.
• Clarity: You make complex technical decisions understandable to both technical and non-technical partners.
• Continuous improvement: You contribute knowledge, documentation and ideas that strengthen how the team works.

For remote positions outside the U.S., compensation is determined based on geographic location, qualifications, experience, and skills.

Joining us is more than saying “yes” to making the world a healthier place. It’s discovering a career that’s challenging, supportive and inspiring. Where a culture driven by excellence helps you not only meet your goals, but also create new ones. We focus on creating a diverse and inclusive culture, encouraging individual expression in the workplace and thrive on the innovative ideas this generates. If this sounds like the workplace for you, apply now! We commit to respond to every applicant.