Senior ML Data Scientist - Wireless People Sensing
Algorized 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 a hybrid or on-site position based in Etoy, Switzerland. Fully remote arrangements are not available. Candidates must be legally authorized to work in Switzerland.
LOCATION
On-Site/Switzerland
EMPLOYMENT TYPE
Full Time
Responsibilities
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Design, develop, and improve machine-learning models and algorithms for wireless people sensing using radar and other sensor signals.
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Play a key role in developing foundational models that can support multiple people-sensing tasks, environments, and sensor configurations.
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Develop signal-processing, machine-learning, and deep-learning methods that transform wireless sensor signals into accurate and robust people-sensing outputs.
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Design model architectures and learning approaches that capture spatial and temporal patterns in wireless sensor data.
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Design experiments, define evaluation methodologies, and analyze model behavior, limitations, and generalization.
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Evaluate and adapt relevant advances in time-series and representation learning to improve model accuracy, robustness, and generalization across environments and sensor configurations.
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Cross functional collaboration with software engineers and MLOps in infrastructure development.
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Mentor junior team members and contribute to the technical direction and data-science practices of the company.
Qualifications
Minimum Requirements
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PhD in Data Science, Computer Science, Wireless Communication, Electrical Engineering, Applied Mathematics, Physics, or a related quantitative field.
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At least 3 years of hands-on experience developing machine-learning or data-science solutions for real-world applications.
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Proven expertise in data science and machine learning, with experienceing statistical and learning-based methods to sensor or time-series data.
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Strong knowledge of signal-processing principles and practical experienceing methods such as spectral analysis, filtering, estimation, detection, tracking, or sensor fusion.
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Strong understanding of machine-learning fundamentals, including model development, evaluation, optimization, and generalization.
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Strong Python skills and practical experience with frameworks such as PyTorch, scikit-learn, NumPy, and SciPy.
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Ability to take ownership of open-ended technical problems and move effectively from exploration to validated solutions.
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Excellent collaboration and communication skills, with genuine enthusiasm for solving challenging people-sensing problems.
Preferred Requirements
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Experience with radar, RF sensing, LiDAR, computer vision, acoustics, or other sensing modalities.
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Experience designing experiments and working with complex, noisy, or imperfect real-world data.
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Familiarity with self-supervised learning, representation learning, multimodal models, or foundation-model development.
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Experience with 3D positioning, occupancy sensing, human activity recognition, tracking systems, or vital-sign estimation.
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Familiarity with edge-AI constraints and the trade-offs involved in moving models from research into production.
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Experience collaborating across data science, embedded, software, and product teams.