Senior ML Data Scientist - Wireless People Sensing

Join Us in Building the Nervous System for Physical AI

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 an on-site position based in Campbell, CA, USA.

RESPONSIBILITIES

• Design, develop, and improve machine-learning models and algorithms for wireless people sensing using radar and other sensor signals.

• Play a key role in developing foundational models that can support multiple people-sensing tasks, environments, and sensor configurations.

• Develop signal-processing, machine-learning, and deep-learning methods that transform wireless sensor signals into accurate and robust people-sensing outputs.

• Design model architectures and learning approaches that capture spatial and temporal patterns in wireless sensor data.

• Design experiments, define evaluation methodologies, and analyze model behavior, limitations, and generalization.

• Evaluate and adapt relevant advances in time-series and representation learning to improve model accuracy, robustness, and generalization across environments and sensor configurations.

• Collaborate cross-functionally with software engineers and MLOps on infrastructure development.

• Mentor junior team members and contribute to the company's technical direction and data-science practices.

QUALIFICATIONS

Minimum Requirements​

• PhD in Data Science, Computer Science, Wireless Communication, Electrical Engineering, Applied Mathematics, Physics, or a related quantitative field.

• At least 3 years of hands-on experience developing machine-learning or data-science solutions for real-world applications.

• Proven expertise in data science and machine learning, with experience applying statistical and learning-based methods to sensor or time-series data.

• Strong knowledge of signal-processing principles and practical experience applying methods such as spectral analysis, filtering, estimation, detection, tracking, or sensor fusion.

• Strong understanding of machine-learning fundamentals, including model development, evaluation, optimization, and generalization.