ML Research Engineer

Research and develop novel models and algorithms that will form the foundation of Circadia's next-generation AI capabilities, including patient activity monitoring, physiological foundation models, radar-based bed-exit detection, and voice-based phenotyping. Stay current with relevant ML research and rapidly prototype ideas from the literature, adapting them to Circadia's problem domains and data modalities. Formulate, design, run, and learn from experiments with scientific rigor, maintaining clear hypotheses, controlled comparisons, and reproducible results. Implement and adapt models to function effectively and efficiently in deployment environments, including both cloud infrastructure and on-device inference on Circadia's clinical monitoring hardware. Work with ML Ops and backend engineering teams to ensure models meet production requirements for latency, memory, reliability, and maintainability. Optimise models for constrained compute environments where needed (e.g. quantisation, distillation, efficient architectures). Work closely with clinical research teams to design validation studies, define performance benchmarks, and generate evidence to support regulatory approval. Help define future-proof technical and data collection requirements in conjunction with clinical and signal processing teams, ensuring research efforts are grounded in clinical utility. Document technical methods, experimental results, and architectural decisions for internal and external consumption. Present research findings to technical and non-technical stakeholders, including clinical partners and leadership. Contribute to publications, white papers, or regulatory submissions as needed.