<div class="show-more-less-html__markup show-more-less-html__markup--clamp-after-5 relative overflow-hidden"> <p><strong>Role Description</strong></p><p><br/></p><p>We are seeking a Senior Machine Learning Engineer to join Sleepiz AG and take ownership of our machine learning initiatives in radar-based health sensing. In this position, you will lead the development and upkeep of models that support vital health applications, guiding them from research to deployment in real-world settings. Your responsibilities include designing clinical data collection strategies, collaborating with our backend team to deploy models, and leading a small team of engineers. We value a versatile candidate with sharp critical thinking: someone who asks the right questions, bridges clinical needs with technical solutions, and thrives amid the ambiguity of building MedTech products.</p><p><br/></p><p> <strong>Key Responsibilities</strong></p><p><br/></p><ul><li>Lead the development of machine learning models for health monitoring applications such as real-time macro motion analysis, long-term trend analysis for predictive modeling taking projects from prototype to production.</li><li>Maintain and improve deployed models by identifying and fixing shortcomings observed during real-world use.</li><li>Design data collection campaigns and clinical studies, including defining sample sizes, endpoints, and analysis plans, and participate in the statistical analysis of results.</li><li>Collaborate with the backend engineering team to ensure seamless deployment, integration, and monitoring of models in production.</li><li>Lead and mentor a small team of ML engineers, setting technical direction and encouraging growth.</li><li>Stay updated on relevant advances in deep learning, signal processing, and clinical ML to continually enhance our approach.</li></ul><p><br/></p><p><strong>Qualifications</strong></p><p><br/></p><ul><li>Extensive industry experience in machine learning: at least 2 years with a PhD, or at least 6 years without.</li><li>Hands-on experience with deep learning, including designing, training, and evaluating neural network architectures.</li><li>Professional experience in healthcare or MedTech, especially working with physiological signals (e.g., cardiac, respiratory, or sleep-related data).</li><li>Strong grounding in statistics, with experience designing studies and analyzing clinical or experimental data.</li><li>Experience deploying and maintaining ML models in production environments, including troubleshooting real-world failure modes.</li><li>Proficiency in Python and familiarity with modern ML frameworks (e.g., PyTorch, TensorFlow).</li><li>Strong problem-solving skills and the ability to ask the right questions when clarifying ambiguous problems.</li><li>Knowledge of software engineering best practices, including testing, version control (Git), and code reviews.</li><li>Past experience leading or mentoring engineers.</li></ul><p><strong>Nice-to-Have</strong></p><p><br/></p><ul><li>Experience with neural network architectures for sequential or time-series data (e.g., transformers, RNNs, temporal convolutional networks).</li><li>Experience with radar-based sensing or contactless monitoring technologies.</li><li>Familiarity with regulatory considerations in MedTech (e.g., FDA, MDR).</li><li>Exposure to MLOps tools and workflows for continuous model deployment and monitoring.</li></ul> </div>