Senior Machine Learning Engineer

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 Med Tech products.

• 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.
• Maintain and improve deployed models by identifying and fixing shortcomings observed during real-world use.
• Design data collection campaigns and clinical studies, including defining sample sizes, endpoints, and analysis plans, and participate in the statistical analysis of results.
• Collaborate with the backend engineering team to ensure seamless deployment, integration, and monitoring of models in production.
• Lead and mentor a small team of ML engineers, setting technical direction and encouraging growth.
• Stay updated on relevant advances in deep learning, signal processing, and clinical ML to continually enhance our approach.

• Extensive industry experience in machine learning: at least 2 years with a PhD, or at least 6 years without.
• Hands‑on experience with deep learning, including designing, training, and evaluating neural network architectures.
• Professional experience in healthcare or Med Tech, especially working with physiological signals (e.g., cardiac, respiratory, or sleep‑related data).
• Strong grounding in statistics, with experience designing studies and analyzing clinical or experimental data.
• Experience deploying and maintaining ML models in production environments, including troubleshooting real‑world failure modes.
• Proficiency in Python and familiarity with modern ML frameworks (e.g., PyTorch, Tensor Flow).
• Strong problem‑solving skills and the ability to ask the right questions when clarifying ambiguous problems.
• Knowledge of software engineering best practices, including testing, version control (Git), and code reviews.
• Past experience leading or mentoring engineers.

• Experience with neural network architectures for sequential or time‑series data (e.g., transformers, RNNs, temporal convolutional networks).
• Experience with radar‑based sensing or contactless monitoring technologies.
• Familiarity with regulatory considerations in Med Tech (e.g., FDA, MDR).
• Exposure to MLOps tools and workflows for continuous model deployment and monitoring.