Senior ML Data Scientist - Wireless People Sensing

Algorized; Campbell (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

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. We are seeking a Senior Data Scientist who loves innovation, applied research, and turning complex sensing challenges into robust products.

Join a dynamic startup with ownership and cross-disciplinary collaboration. This is a hybrid or on-site role based in Campbell, CA, USA.

Qualifications

• PhD in Data Science, CS, 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.
• Strong Python skills and practical experience with frameworks such as PyTorch, scikit-learn, NumPy, and SciPy.
• Ability to take ownership of open-ended technical problems and move effectively from exploration to validated solutions.
• Excellent collaboration and communication skills, with genuine enthusiasm for solving challenging people-sensing problems.

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.
• Cross functional collaboration with software engineers and MLOps in infrastructure development.
• Mentor junior team members and contribute to the technical direction and data-science practices of the company.

Skills

Python

PyTorch

scikit-learn

NumPy

SciPy

Time-series

Sensor data

Model development

Evaluation

Generalization

Education

PhD in quantitative field

Tools

PyTorch

scikit-learn

NumPy

SciPy

Job description

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 Campbell, CA, USA. Fully remote arrangements are not available. Candidates must be legally authorized to work in Switzerland.

LOCATION

On-Site/Campbell, CA

EMPLOYMENT TYPE

Full Time

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.


• Cross functional collaboration with software engineers and MLOps in infrastructure development.


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

Qualifications

• 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.


• Strong Python skills and practical experience with frameworks such as PyTorch, scikit-learn, NumPy, and SciPy.


• Ability to take ownership of open-ended technical problems and move effectively from exploration to validated solutions.


• Excellent collaboration and communication skills, with genuine enthusiasm for solving challenging people-sensing problems.

Preferred Requirements

• Experience with radar, RF sensing, LiDAR, computer vision, acoustics, or other sensing modalities.


• Experience designing experiments and working with complex, noisy, or imperfect real-world data.


• Familiarity with self-supervised learning, representation learning, multimodal models, or foundation-model development.


• Experience with 3D positioning, occupancy sensing, human activity recognition, tracking systems, or vital-sign estimation.


• Familiarity with edge-AI constraints and the trade-offs involved in moving models from research into production.


• Experience collaborating across data science, embedded, software, and product teams.