RF Modeling Manager

Motorola Solutions•Richardson, TX

1d•Onsite

About The Position

Motorola Solutions' innovations, products, and services play essential roles in people's lives. Our end-to-end suite of software solutions helps customers manage emergency communications, process video and evidence, and leverage cutting-edge AI-driven analytics for security and operational insights. We are industry leaders in video security and analytics, with solutions deployed in more than 120 countries across diverse environments such as school campuses, transportation systems, healthcare centers, public venues, critical infrastructure, prisons, factories, casinos, airports, financial institutions, government facilities, and retailers. Our AI-powered security solutions integrate advanced video analytics, machine learning, and embedded intelligence to enable proactive threat detection, enhanced situational awareness, and automated decision-making.

Requirements

Master’s degree with 6+ years of relevant work or a PhD (preferred) with 4+ years of professional experience in AI/ML, Electrical Engineering, CS, Applied Math, Robotics, or a related technical discipline.
Solid understanding of RF propagation, indoor multipath, path-loss modeling, and RTT distance estimation.
Experience with filtering, Kalman/EMA smoothing, noise modeling, and time-series feature extraction.
Strong Matlab, Python skills (NumPy, SciPy, Pandas, scikit-learn) and experience working with Wi-Fi RSSI, BLE RSSI, RTT/FTM, IMU datasets.
Hands-on experience with clustering, probabilistic modeling, similarity metrics, and lightweight ML classification/regression.
Experience deploying algorithms to real-time, enterprise-scale systems with tight latency constraints.
Able to analyze noisy data, design robust models, validate hypotheses, and convert prototypes into production-ready logic.

Nice To Haves

PhD (preferred)

Responsibilities

Architect and develop AI-driven models for indoor localization, including fingerprinting, similarity scoring, probabilistic grid-cell prediction, and lightweight sensor fusion
Build and refine path-loss, RF propagation, and multipath-aware models to improve accuracy, robustness, and stability
Apply advanced signal processing techniques, filtering, smoothing, noise reduction, time-series modeling to transform raw RF and IMU data into high-quality features
Combine Wi-Fi RSSI, BLE RSSI, RTT timestamps, and IMU patterns to produce hybrid models that outperform single-sensor approaches
Evaluate accuracy using ground-truth traces, run controlled experiments, tune hyperparameters, and improve model confidence scoring
Deploy models into real-time scoring pipelines, collaborating with cloud engineering to ensure sub-100ms inference and large-scale reliability
Work closely with firmware, data, RF, QA, and product teams
Mentor peers in algorithmic reasoning and modeling excellence