Machine Learning Engineer

Circadia Health; London; Hybrid; GBP 50,000 - 80,000; Full time

Job summary

Circadia Health is seeking a Machine Learning Engineer to design and build machine learning pipelines for healthcare applications. You will be responsible for optimizing workflows and collaborating closely with product teams to create impactful ML-driven products. Join an innovative team working on cutting-edge healthcare solutions that enhance patient outcomes.

Benefits

Private health insurance

28 days paid annual leave

Learning and personal development courses

Opportunity to attend conferences

New laptop of choice

Flexible / hybrid working

Regular team events

Qualifications

• Mastery of computer science fundamentals and core machine learning concepts.
• Responsible for mission-critical healthcare machine learning models.
• Ability to liaise with both technical and non-technical stakeholders.

Responsibilities

• Design, build, and maintain end-to-end machine learning pipelines.
• Develop, deploy, and maintain scalable pipelines for ML models.
• Optimize model training and inference workflows for large-scale data.

Skills

Technical acumen

Detail oriented

Communication skills

Organization

Growth Mindset

Job description

Join to apply for the Machine Learning Engineer role at Circadia Health

Join to apply for the Machine Learning Engineer role at Circadia Health

Circadia Health is a healthcare technology company that has developed the world's first FDA cleared contactless remote patient monitoring system and AI-powered early detection system. Powered by cutting-edge technology and AI, the system allows for the early detection of medical events such as Congestive Heart Failure, COPD Exacerbations, Pneumonia, Sepsis, UTIs, and Falls. We're monitoring over 30,000+ patients daily and growing rapidly. As we scale our team, Circadia is looking for energetic, personable, and solutions-oriented individuals driven by creating the ultimate customer experience. Prior experience in healthcare is a big plus, but not required. Our mission is to enhance patient outcomes and improve healthcare processes by providing cutting-edge solutions to healthcare providers and patients alike.

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.
• 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

Seniority level

• Seniority level

  Associate

Employment type

• Employment type

  Full-time

Job function

• Industries

  Hospitals and Health Care

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