Machine learning, personalized health, and predictive medicine. If these areas resonate with you, join us to work on extremely motivating challenges at Spiden. Spiden is a Swiss MedTech venture with the vision to use state-of-the-art detection techniques to continuously monitor and learn from a wide range of vital indicators, to better manage chronic diseases, to customize critical treatments and, to improve your health.
Using proprietary optical sensors, Spiden is building a cutting-edge biomedical data generation pipeline to power our Machine Learning prediction algorithms. To achieve our vision, our team and advisory board consist of world experts coming from top academic institutions (ETH, EPFL, Columbia, Princeton, or Harvard among others) and industry leaders (Baxter, Roche, Lonza). We are looking for a talented, experienced, voraciously curious, and self-driven ML Engineer to play a central role in building it with great opportunities for growth.
As a Machine Learning Engineer, you will design and develop ML products that involve large-scale data processing using an advanced ML technology stack. You will lead architecture design and ML infrastructure.
In this role, you will be part of the Machine Learning Engineering team working closely with all RnD teams, including Biomedical Science, Biochemistry, Biophotonics, and Electrical Engineering.
- Triage issues and debug/track/resolve them by analyzing the sources of issues and the impact on medical equipment, hardware, network, or service operations and quality
- Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency)
- Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies
- Communicate effectively mainly in person but as well via video conferencing tools, experience with technical reviews, and coordination with external third parties such as partners and suppliers
- Progressively, as we transition out of R&D phase to industrialization and product launch, deploy and use various big data technologies and run pilots to design low latency MLOps architectures