Cairns Health (https://www.cairns.ai/) is creating a fundamentally better healthcare experience for people with chronic health conditions and those who care for them. We make healthcare more accessible by simplifying complex care plans, connecting care teams and meeting patients where they live. Through our conversational AI, patients use their voice to interact with our digital care companion, who proactively gives medication reminders, symptom checks, behavioral nudges and even engages in friendly conversation to ease loneliness. Cairns uses a device that includes radar to put the patient in context and passively monitors their activities, including: heart rate, breathing rate and sleep stages, all without a wearable. The result is informed and timely intervention that drives improved clinical outcomes, reduced care delivery costs and a more satisfactory healthcare experience for all.

About the Role

We are seeking a highly skilled AI/ML Engineer to join our team, focusing on the development of cutting-edge radar-based systems for vital signs inference and contextual activity tracking (e.g., detecting whether someone is standing, sitting, walking, in the room, or in bed). This role is ideal for someone who thrives in a startup environment, is comfortable working independently, and is motivated to push the boundaries of applied AI in healthcare and sensing technologies.

Responsibilities

Model Development & Deployment

• Design, train, and deploy machine learning models for radar signal interpretation and human activity/vital signs inference.
• Implement end-to-end ML pipelines including data collection, preprocessing, training, testing, validation, and deployment.
• Manage the ML lifecycle, including monitoring for drift, retraining, and updating models in production environments.

Signal Processing & Radar Systems

• Apply advanced signal processing techniques to radar data for robust and accurate feature extraction.
• Collaborate with hardware and embedded software engineers to ensure seamless integration between ML models and radar systems.

Edge Deployment

• Optimize and deploy ML models on embedded hardware platforms, with a focus on C++ edge deployment.
• Build efficient inference pipelines that run within constrained compute environments.

Collaboration & Problem Solving

• Work cross-functionally with hardware, software, and clinical teams to define requirements and deliver production-ready solutions.
• Operate independently in a startup setting, proactively solving problems and driving projects from concept to deployment.

Qualifications

Education & Experience

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Applied Mathematics, or related field.
• 5–7 years of professional experience in applied AI/ML, ideally with significant experience in startups.

Technical Skills

• Strong expertise in machine learning & deep learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
• Proficiency in Python for model development and experimentation.
• Strong proficiency in C++, with proven experience deploying models in embedded/edge environments.
• Background in signal processing, with a strong preference for radar systems experience.
• Experience with ML lifecycle management: data collection, cleansing, training, validation, monitoring, drift detection, and retraining.

Soft Skills

• Demonstrated ability to work independently and thrive in fast-paced, startup environments.
• Strong problem-solving skills, creativity, and a can-do attitude.
• Excellent communication skills for collaborating across technical and non-technical teams.

Bonus Experience (Nice-to-Have)

• Familiarity with healthcare or human monitoring applications.
• Experience with sensor fusion (combining radar with other sensing modalities).
• Knowledge of cloud-to-edge ML deployment strategies.

Why Join Us?

• Work on cutting-edge technology that impacts real-world healthcare and wellness.
• Join a small, fast-moving team where your contributions will have immediate impact.
• Opportunity to shape the future of radar-based AI systems and help bring them into production at scale.