Company Overview Position Overview As a Machine Learning Engineer, you will design, build, and maintain end-to-end machine learning pipelines, transforming experimental models into scalable, production-ready systems while closely collaborating with the Product Design and Engineering (PDE) team to create impactful ML-driven products in the healthcare setting. In addition to optimizing infrastructure, automating workflows, and ensuring seamless integration from model development to deployment, you will play a key role in building and iterating on the actual products that leverage machine learning to deliver value to patients and healthcare professionals. With a strong focus on scalability, performance and you will help bridge the gap between cutting-edge algorithms and real-world applications in a fast-paced, startup environment - driving our mission of saving lives. Key Responsibilities: Ownership of Machine Learning Infrastructure: Develop, deploy, and maintain scalable pipelines for both Circadia’s proprietary ML models and off-the-shelf solutions. Optimize model training and inference workflows to handle large-scale, real-time data efficiently. Design robust model monitoring systems to track performance, detect drift, and ensure reliability. Implement infrastructure to support the experimentation and productionization of ML models cost-effectively in AWS and Snowflake. Building and Deploying ML-Driven Products: Collaborate closely with the Product Design and Engineering (PDE) team to design, build, and iterate on ML-powered products. Translate complex machine learning algorithms into user-facing features and services. Work with key stakeholders to ensure alignment between technical implementation and product goals. Define and develop APIs for seamless integration of ML models with product functionalities. Orchestration of Scalable ML Pipelines: Engage with data and ML scientists to plan the architecture for end-to-end machine learning workflows. Implement scalable training and deployment pipelines using tools such as Apache Airflow and Kubernetes. Perform comprehensive testing to ensure reliability and accuracy of deployed models. Develop instrumentation and automated alerts to manage system health and detect issues in real-time. Attributes: Technical acumen: Mastery of computer science fundamentals and understanding of core machine learning concepts. Detail oriented: Responsible for mission-critical healthcare machine learning models. Communications and Trust: Good communication skills with the ability to liaise with both technical and non-technical stakeholders. Organization and Getting Stuff Done: Juggling multiple projects and timelines. Prioritizing. Keen eye for detail in all tasks and projects. Growth Mindset: Your ability to learn from mistakes, reflect on mistakes, and not make mistakes again. Being curious and asking questions and showing resilience in the face of setbacks. Benefits: Join an energetic, diverse team dedicated to working towards the challenge of improving and saving patient lives Private health insurance with Vitality Health for you and your family, including discounted gym memberships, wellness retreats, fitness devices, and lots more 28 days paid annual leave during each holiday year (including bank holidays) Fully financed learning and personal development courses to help you grow in your role Opportunity to attend conferences and acquire certifications, paid for by the company New laptop of your choice for you to work on either at home, our at Circadia’s London Bridge office Flexible / hybrid working to suit your personal circumstances and allow you to be productive wherever you are most comfortable working Participate in and help plan regular team events, lunches and dinners