
Backed by years of academic research, Algorized enables a new world of sensing and positioning applications through a software-only upgrade to existing commodity sensors. The Algorized platform uses proprietary ML and algorithms to unlock a massive amount of data available to its customers, including real-time accurate positioning, vital sign detection in any physical environment. Its software-only solution powers innovative applications across industries, from robotics to automotive, using UWB radar and wireless sensors.

Backed by years of academic research, Algorized enables a new world of sensing and positioning applications through a software-only upgrade to existing commodity sensors. The Algorized platform uses proprietary ML and algorithms to unlock a massive amount of data available to its customers, including real-time accurate positioning, vital sign detection in any physical environment. Its software-only solution powers innovative applications across industries, from robotics to automotive, using UWB radar and wireless sensors.
What they do: People-sensing edge-AI software that turns commodity wireless sensors (UWB, mmWave, Wi‑Fi) into positioning, tracking and vital-sign detection systems
Founded / HQ: Founded 2024 (profiles vary); headquartered in Campbell, California
Stage / investors: Seed stage; lead investor listed as Amazon Industrial Innovation Fund
Applications: Robotics safety, in-car occupant sensing, industrial human–machine interaction, smart infrastructure
Sensing and perception for human presence, positioning, tracking and vital-sign monitoring using wireless sensors.
2024
Software Development
“Has corporate strategic investor (Amazon Industrial Innovation Fund) alongside a syndicate including SICTIC, shuckerVC, Acrobator Ventures, Berkeley SkyDeck Fund, Monte Carlo Capital and CoreNest Capital”
Company Description
Algorized is a fast-growing deep-tech startup developing cutting-edge software for human positioning and sensing. By leveraging advanced algorithms, edge ML, radar, and sensor fusion, we enable precise people tracking, positioning, vital sign detection, and age classification.
We are seeking an ML Engineer to design, build, and maintain scalable machine learning algorithms and infrastructure to support the creation and deployment of models that process data from sensors, radar, and cameras. This role will be critical in ensuring high performance, real-time accuracy, and seamless ML model performance on the edge.
Key Responsibilities
Design, improve and implement complex machine learning models and algorithms processing raw radar sensor data and other sensor data to address customer business needs
Manage the ML data pipeline, including data collection, extraction, validation, and preprocessing.
Format, restructure, or validate datasets to ensure high data quality, and readiness for analysis
Develop robust data preprocessing methods to enhance model accuracy and efficiency
Analyze extensive datasets to identify trends and patterns and interpret them
Build the foundation for highly scalable data collection and analytics
Design scalable ML pipelines to support sensor-agnostic people-sensing model training, testing, deployment, and monitoring
Implement monitoring tools to track model accuracy, performance, and scaling issues
Minimum Qualifications
MSc or advanced degree in a relevant field
Experienced in algorithm design and optimization
5+ years hands-on experience in a similar position (3+ for candidates with PhD)
Expertise in sensor fusion, edge AI, or embedded ML models for real-time applications
Ability to collaborate efficiently with backend developers
Good coding skills in Python, familiarity with PyTorch, scikit-learn and other Python ML libraries
Experience with time-series data, sensor data pipelines, or stream processing frameworks
Good understanding of ML model deployment, CI/CD and monitoring frameworks
Excellent teamwork & communication skills, with a passion for innovation and solving complex challenges
Genuine interest in solving challenging people-sensing problems from start to finish
Willingness to travel domestically and internationally for development and on-site customer support
Preferred Qualifications
PhD in the relevant field
Experience with signal processing and edge ML models
Experience with ML for radar / LiDAR / computer vision applications
Experience deploying ML models in C/C++
Familiarity with 3D positioning, tracking systems, or vital signs estimation
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