ML Research Engineer

Key Responsibilities

• 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.

Required Qualifications

• Master's degree in Computer Science, Machine Learning, Data Science, Mathematics, or another highly quantitative field.
• Ability to write production-grade, maintainable code in Python.
• Solid understanding of classical machine learning techniques with experience applying them to real-world problems.
• Strong knowledge of deep learning methods and frameworks (e.g. PyTorch, TensorFlow, JAX) with an ability to quickly implement research papers into production-grade code.
• Strong scientific mindset: ability to rapidly iterate by formulating, running, and learning from experiments.
• Strong written and oral communication skills, both technical and non-technical.

Preferred Qualifications

• 3+ years of experience in an ML role with both research and engineering components.
• PhD in Computer Science, Machine Learning, Data Science, Mathematics, or another highly quantitative field.
• Experience with cloud computing platforms (e.g. AWS, GCP, Azure) and deployment of models into production (e.g. Docker, Flask, FastAPI).
• Experience working with data from IoT devices or sensors (e.g. radar, PPG, ECG),  particularly in a medical or health context.
• Experience with (or openness to) accelerating work using AI coding tools.
• Evidence of exceptional competence through one or more of: high-quality first-author publications in AI/ML, significant open-source contributions, strong performance in ML competitions, or standout hackathon results.

What You Bring

• You combine research creativity with engineering discipline - you're as comfortable reading papers as you are shipping code.
• You think in experiments: you form hypotheses, test them rigorously, and iterate quickly.
• You care about clinical impact and are motivated by building technology that directly improves patient care.
• You're comfortable working in a startup environment where you'll move fast and operate with high autonomy.
• You communicate complex technical ideas clearly to both engineers and clinicians.