Senior ML Data Scientist - Wireless People Sensing

Join Us in Building the Nervous System for Physical AIAlgorized is a VC-funded Silicon Valley deep-tech company with Swiss roots. We build edge-AI models that give robots real-time awareness of people using existing wireless sensors, enabling safer human-machine collaboration.As we continue to scale, we are looking for a Senior Data Scientist who is passionate about innovation, applied research, and turning complex sensing challenges into robust products. If you thrive in a dynamic startup environment, take ownership, and enjoy working across data science, signal processing, and product development, we would love to meet you.This is an on-site position based in Etoy, Switzerland. Fully remote arrangements are not available.ResponsibilitiesDesign, develop, and improve machine-learning models and algorithms for wireless people sensing using radar and other sensor signalsPlay a key role in developing foundational models that can support multiple people-sensing tasks, environments, and sensor configurationsDevelop signal-processing, machine-learning, and deep-learning methods that transform wireless sensor signals into accurate and robust people-sensing outputsDesign model architectures and learning approaches that capture spatial and temporal patterns in wireless sensor dataDesign experiments, define evaluation methodologies, and analyze model behavior, limitations, and generalizationEvaluate and adapt relevant advances in time-series and representation learning to improve model accuracy, robustness, and generalization across environments and sensor configurationsCollaborate cross-functionally with software engineers and MLOps on infrastructure developmentMentor junior team members and contribute to the company's technical direction and data-science practicesMinimum RequirementsPhD in Data Science, Computer Science, Wireless Communication, Electrical Engineering, Applied Mathematics, Physics, or a related quantitative fieldAt least 3 years of hands-on experience developing machine-learning or data-science solutions for real-world applicationsProven expertise in data science and machine learning, with experience applying statistical and learning-based methods to sensor or time-series dataStrong knowledge of signal-processing principles and practical experience applying methods such as spectral analysis, filtering, estimation, detection, tracking, or sensor fusionStrong understanding of machine-learning fundamentals, including model development, evaluation, optimization, and generalizationStrong Python skills and practical experience with frameworks such as PyTorch, scikit-learn, NumPy, and SciPyAbility to take ownership of open-ended technical problems and move effectively from exploration to validated solutionsExcellent collaboration and communication skills, with genuine enthusiasm for solving challenging people-sensing problemsPreferred RequirementsExperience with radar, RF sensing, LiDAR, computer vision, acoustics, or other sensing modalitiesExperience designing experiments and working with complex, noisy, or imperfect real-world dataFamiliarity with self-supervised learning, representation learning, multimodal models, or foundation-model developmentExperience with 3D positioning, occupancy sensing, human activity recognition, tracking systems, or vital-sign estimationFamiliarity with edge-AI constraints and the trade-offs involved in moving models from research into productionExperience collaborating across data science, embedded, software, and product teamsWe may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. #J-18808-Ljbffr