Machine Learning Engineer (Healthcare) at Circadia Health (London, UK)

Circadia Health is a medical technology company with an FDA cleared product: The Circadia C100 Contactless Respiratory Monitoring System. Circadia develops proprietary hardware, software, and algorithms for early detection of respiratory failure the third leading cause of death. Founded in 2016 by Imperial College alumni, Circadia is venture backed by Silicon Valley VCs, including Village Global, the fund backed by Bill Gates, Jeff Bezos, and Mark Zuckerberg. Circadia is headquartered in London with offices in Karachi and Los Angeles. WSJ article here: https://www.wsj.com/articles/circadia-gets-fda-nod-for-ai-powered-respiratory-monitor-11593077402?st=2izwgpz16liva1a&reflink=article_copyURL_share

Responsibilities:

  • Developing and orchestrating core ML pipelines, from experimentation to training, evaluation, benchmarking and deployment, with emphasis on scalability and reproducibility.
  • Developing data collection, data labelling and other internal ML tooling together with Backend Engineers and Data Scientists.
  • Deploying models into production, monitoring their performance and developing ‘human in the loop’ processes for continuous improvement.
  • Architecting and deploying processes to increase the speed of experimentation and model deployment within the DS/ML teams.
  • Working in a multidisciplinary team of Data Scientists, ML Scientists to develop a cutting edge radar-based, contactless patient monitoring system.

Benefits

  • A fast-paced and collaborative environment
  • Work with world-class engineers, researchers and clinicians
  • Get your hands on real-word patient data
  • Hybrid remote and office work
  • Build category-defining products
  • A chance to be an early member of a fast-growing company

Skills & Requirements

  • Masters in Computer Science / Software Engineering with 3+ years of experience in software development.
  • Experience in deploying and monitoring ML models and pipelines in production environments.
  • Experience managing the entire ML life cycle and building scalable ML systems.
  • Core proficiency in Python and SQL. Competency in other languages and frameworks is a bonus.
  • Experience working with any major cloud services provider (eg: AWS, GCP, Azure). We use AWS.
  • Competency in data engineering and storage frameworks such as Spark, S3, MySQL or TimeScaleDB.

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