
Machine Learning Engineer - Senior / Principal/ Senior Principal
- Australia
- Permanent
- Full-time
- 3-5 or 5+ years of experience working as a machine learning engineer delivering products in real world applications.
- Demonstrated experience in designing and implementing scalable ML solutions, frameworks and pipelines for production.
- Demonstrated experience in architecture design, deployment/monitoring, API and service engineering, workflow and tooling development.
- Demonstrated experience in leveraging LLMs (such as code assist) to accelerate the above development activities.
- Good technical understanding of Large Language Model, Machine Learning / Deep Learning architectures like Transformers, training methods, and optimizers.
- Proven experience in coaching teams on engineering practices and PR reviews.
- Commitment to staying up to date with the field and applying latest technologies to solve complex business problems and bringing them into production.
- Knowledge of healthcare and experience delivering healthcare AI products are a significant plus.
- Having referrable products delivered before is a significant plus.
- Masters or bachelor's in computer science or related field with 3-5 or 5+ years relevant experience
- AI Product Development: Partner with product managers to translate business and healthcare requirements into actionable AI projects.
- Technical Leadership: Collaborate with technical leaders and multinational AI teams to deliver high-impact features and services on schedule.
- Cross-Functional Collaboration: Engage with both internal medical experts and customers to deeply understand healthcare contexts and inform AI solution development.
- Project Delivery: Drive projects from concept to production, participating in planning, review, and retrospective meetings.
- System Architecture and Infrastructure: Design and build scalable systems to support data processing, training, and deployment of generative AI models, while efficiently managing compute resources.
- Deployment and Monitoring: Automate deployment of generative models to production and set up monitoring to track their performance, reliability, and operational health.
- API and Service Engineering: Develop performant, secure APIs and services that enable seamless integration and scalable access to generative AI model capabilities.
- Development Workflow and Tooling: Create tools and workflows that ensure experiment reproducibility, dataset management, and smooth collaboration throughout the model lifecycle.
- Software Engineering Best Practices: Apply software engineering standards - such as modular code, testing, and documentation - to ensure maintainability and stability of ML systems.
- Hands-on Programming and Code Review: Perform day to day programming task, deliver production quality codes, do PR reviews and set bars for PR review process, advocating for maintainability and robustness.
- Mentorship: Lead and mentor both junior and senior applied scientists, fostering growth and technical excellence.