ML Research Engineer

Position Overview

As an ML Research Engineer at Circadia Health, you will research, design, and build the next generation of models and algorithms that power our clinical monitoring platform. Circadia's devices use radar to continuously and contactlessly capture respiratory rate, heart rate, and movement data from thousands of patients – alongside audio and other physiological signals. This continuous sensing data is paired with deep clinical context from EHR integrations including conditions, medications, clinical notes, and care events, resulting in a dataset of extraordinary scale and depth that we've only begun to tap. Your work will push into novel problem domains: physiological foundation models, patient activity monitoring, radar-based bed-exit detection, and voice-based phenotyping – turning research ideas into production-grade systems that run on Circadia's devices and cloud infrastructure.

Reporting to the Principal ML Engineer, you will work at the intersection of research and engineering: formulating hypotheses, designing experiments, implementing models, and deploying them into real clinical environments. You will collaborate closely with clinical research, signal processing, and data teams to validate algorithms, define data collection requirements, and support regulatory approval.

This role requires a strong scientific mindset paired with a deployment-first mentality. We're looking for someone who can rapidly translate research papers into working code, iterate through experiments with rigor, and ship models that perform reliably on real patient data.

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.

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.

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.

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.

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