About Your Role

Reporting to the Head of AI Engineering, the Senior/Principal ML Engineer translates the latest advances in artificial intelligence, including foundation models and self-supervised learning, into AI-as-a-medical-device products. Our AI Engineers work in dynamic teams alongside Software Developers, Testers, Cloud DevOps and Integration Engineers. We're looking for a self-starter whose passion for learning shows in the adoption of new tools and best practice. As part of our R&D team, we offer the unique opportunity to make a direct, meaningful impact on the lives of patients and their families.

What You'll Do:

• Develop AI algorithms, prototypes and solutions for healthcare, with a focus on foundation models and self-supervised learning;
• Optimise models and training pipelines for accuracy, scale and rapid experimentation;
• Follow agile methodology and software engineering best practice, focussing on test-driven development, rapid prototyping, validation and iteration;
• Provide regular technical and other progress reports relevant to projects, and ensure all progression is properly documented;
• Engage with the literature to benchmark against and adopt state-of-the-art techniques and algorithms;
• Rigorously evaluate generative AI models, and partner closely with teams training models at scale; and
• Contribute to a culture of excellence, helping to solve problems as they arise, instil a culture of best practice, integrity and agility, as well as champion the Harrison mission internally and externally.

What You'll Bring:

• Extensive experience in an ML engineering or applied research role, working closely with software development teams;
• Extensive experience with Python and modern deep learning frameworks such as PyTorch;
• Hands-on experience building foundation models and self-supervised learning methods;
• Honours or higher academic qualification in computer science, and/or strong quantitative grounding and equivalent software engineering work experience; and
• Experience evaluating generative AI models and partnering with model training at scale.

Nice to have skills and characteristics:

• Experience with healthcare data and solutions;
• Practical experience in building, deploying and testing ML models in a product development context using software engineering best practices;
• Experience with large-scale distributed training, AWS cloud development, and/or computer vision and natural language processing;
• Experience with a range of AI tools and techniques;
• PhD in ML or equivalent research experience; and
• Publications or open-source contributions in foundation models, self-supervised learning or generative AI.
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