Principal Staff Engineer – Indoor Intelligence & Sensor Fusion

Company Overview

At Motorola Solutions, we believe that everything starts with our people. We’re a global close-knit community, united by the relentless pursuit to help keep people safer everywhere. We build and connect technologies to help protect people, property and places. Our solutions foster the collaboration that’s critical for safer communities, safer schools, safer hospitals, safer businesses, and ultimately, safer nations. Connect with a career that matters, and help us build a safer future.

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.

We're seeking a highly skilled Principal Staff Engineer – Indoor Location Intelligence & Sensor Fusion with in-depth expertise in RF modeling, signal processing, and applied AI/ML. This is a hands‑on role where you will design and optimize the algorithms that power our next‑generation indoor location platform. You’ll work at the intersection of Wi‑Fi, BLE, RTT (802.11mc), CSI, and IMU sensors, transforming noisy real‑world RF data into accurate, real‑time positioning at scale.

• Model & Innovate: Architect and develop AI‑driven models for indoor localization, including fingerprinting, similarity scoring, probabilistic grid‑cell prediction, and lightweight sensor fusion.
• Understand the Physics: Build and refine path‑loss, RF propagation, and multipath‑aware models to improve accuracy, robustness, and stability.
• Extract the Signal: Apply advanced signal‑processing techniques, filtering, smoothing, noise reduction, and time‑series modeling to transform raw RF, CSI subcarrier data, and IMU inputs into high‑quality features.
• Fuse Intelligence: Combine Wi‑Fi RSSI, Channel State Information (CSI), BLE RSSI, RTT timestamps, and IMU patterns to produce hybrid models that outperform single‑sensor approaches.
• Experiment Relentlessly: Evaluate accuracy using ground‑truth traces, run controlled experiments, tune hyperparameters, and improve model confidence scoring.
• Operationalize Intelligence: Deploy models into real‑time scoring pipelines, collaborating with cloud engineering to ensure sub‑100ms inference and large‑scale reliability.
• Collaborate & Elevate: Work closely with firmware, data, RF, QA, and product teams. Mentor peers in algorithmic reasoning and modeling excellence.

• Education:
  
  Master’s degree with 8+ years of relevant work or a PhD (preferred) with 6+ years of professional experience in AI/ML, Electrical Engineering, CS, Applied Math, Robotics, or a related technical discipline.
• RF & Sensors:
  Solid understanding of RF propagation, indoor multipath, path‑loss modeling, RTT distance estimation, and Channel State Information (CSI) extraction.
• CSI & Fine‑Grained Sensing:
  Practical knowledge of using subcarrier‑level Wi‑Fi CSI amplitude and phase metrics to resolve multipath interference and improve positioning resolution beyond RSSI.
• Signal Processing Specialist:
  Experience with filtering, phase unwrapping, Kalman/EMA smoothing, noise modeling, and time‑series feature extraction.
• Data Wrangler:
  Strong Matlab, Python skills (Num Py, Sci Py, Pandas, scikit‑learn) and experience working with Wi‑Fi RSSI, CSI datasets, BLE RSSI, RTT/FTM, and IMU streams.
• AI/ML Engineer:
  Hands‑on experience with clustering, probabilistic modeling, similarity metrics, and lightweight ML classification/regression.
• Production…