{"id":1512852,"url":"https://alion.io/job/algorized-senior-ml-data-scientist-wireless-people-sensing-2","title":"Senior ML Data Scientist - Wireless People Sensing","company":{"id":3790522,"name":"Algorized","domain":"algorized.com","url":"https://alion.io/company/algorized","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Etoy, Switzerland"],"countries":["CH"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":107000,"max_usd":278000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2216},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Computer Vision","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NumPy","optional":false},{"name":"Physical AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SciPy","optional":false},{"name":"Self-Supervised Learning","optional":false},{"name":"Sensor Fusion","optional":false},{"name":"Time Series Forecasting","optional":false}],"status":"live","first_seen_at":"2026-09-29T16:30:50Z","employer_posted_date":"2026-09-29","last_verified_at":"2026-10-20T08:59:55Z","board_verified":true,"closed_at":null,"days_open":20,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":20},"description":"Join Us in Building the Nervous System for Physical AI\nAlgorized 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.\nAs 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.\nThis is an on-site position based in Etoy, Switzerland. Fully remote arrangements are not available.\nRESPONSIBILITIES\nDesign, develop, and improve machine-learning models and algorithms for wireless people sensing using radar and other sensor signals.\nPlay a key role in developing foundational models that can support multiple people-sensing tasks, environments, and sensor configurations.\nDevelop signal-processing, machine-learning, and deep-learning methods that transform wireless sensor signals into accurate and robust people-sensing outputs.\nDesign model architectures and learning approaches that capture spatial and temporal patterns in wireless sensor data.\nDesign experiments, define evaluation methodologies, and analyze model behavior, limitations, and generalization.\nEvaluate and adapt relevant advances in time-series and representation learning to improve model accuracy, robustness, and generalization across environments and sensor configurations.\nCollaborate cross-functionally with software engineers and MLOps on infrastructure development.\nMentor junior team members and contribute to the company's technical direction and data-science practices.\nQUALIFICATIONS\nMinimum Requirements\nPhD in Data Science, Computer Science, Wireless Communication, Electrical Engineering, Applied Mathematics, Physics, or a related quantitative field.\nAt least 3 years of hands-on experience developing machine-learning or data-science solutions for real-world applications.\nProven expertise in data science and machine learning, with experience applying statistical and learning-based methods to sensor or time-series data.\nStrong knowledge of signal-processing principles and practical experience applying methods such as spectral analysis, filtering, estimation, detection, tracking, or sensor fusion.\nStrong understanding of machine-learning fundamentals, including model development, evaluation, optimization, and generalization.\nStrong Python skills and practical experience with frameworks such as PyTorch, scikit-learn, NumPy, and SciPy.\nAbility to take ownership of open-ended technical problems and move effectively from exploration to validated solutions.\nExcellent collaboration and communication skills, with genuine enthusiasm for solving challenging people-sensing problems.\nPreferred Requirements\nExperience with radar, RF sensing, LiDAR, computer vision, acoustics, or other sensing modalities.\nExperience designing experiments and working with complex, noisy, or imperfect real-world data.\nFamiliarity with self-supervised learning, representation learning, multimodal models, or foundation-model development.\nExperience with 3D positioning, occupancy sensing, human activity recognition, tracking systems, or vital-sign estimation.\nFamiliarity with edge-AI constraints and the trade-offs involved in moving models from research into production.\nExperience collaborating across data science, embedded, software, and product 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