This role pays less than
94%
of similar roles. Most pay
$174,600–$223,775
— the shaded band above.
At the midpoint, this role pays about
$150k
versus about
$199k
for comparable roles.
Based on 240 similar postings.
Employer
About Motorola Solutions
Motorola Solutions, Inc. (NYSE: MSI) is a leading American technology company providing mission-critical communications, video security, and analytics for public safety and enterprise customers.
Motorola Solutions currently has
135 open roles
on FindRole.
Listed pay typically runs
$106,500–$140,000
across 117 roles with salary data.
Principal Staff Engineer – Indoor Location Intelligence & Sensor Fusion joins the team to design and optimize algorithms for a next-generation indoor location platform. This hands-on role involves transforming noisy real-world RF data into accurate, real-time positioning by developing AI-driven models for fingerprinting, similarity scoring, and probabilistic grid-cell prediction. The engineer will build path-loss and multipath-aware models while applying advanced signal processing techniques like filtering, smoothing, and time-series modeling to raw Wi-Fi CSI subcarrier data, BLE RSSI, RTT timestamps, and IMU inputs. Key technical requirements include proficiency in Python, Matlab, NumPy, SciPy, and scikit-learn to manage complex datasets. The role addresses the challenge of indoor localization by fusing multiple sensor types into hybrid models that improve accuracy and reliability within high-interference environments while meeting strict sub-100ms inference constraints for large-scale production systems.
What does a Engineer earn in Texas?
Median $182925 from 32 postings across 8 companies.
Architect and develop AI-driven models for indoor localization including fingerprinting and probabilistic grid-cell prediction.
Build and refine path-loss, RF propagation, and multipath-aware models to improve positioning accuracy and stability.
Apply advanced signal processing techniques like filtering, smoothing, and noise reduction to raw RF and IMU data.
Create hybrid models by fusing Wi-Fi RSSI, CSI, BLE, RTT timestamps, and IMU patterns.
Evaluate model accuracy using ground-truth traces and perform hyperparameter tuning through controlled experiments.
Deploy models into real-time scoring pipelines with sub-100ms inference for large-scale reliability.
Mentor peers in algorithmic reasoning and modeling excellence.
What we're looking for
Master’s degree with 6+ years or a PhD with 4+ years of experience in AI/ML, Electrical Engineering, CS, Applied Math, Robotics, or related fields.
Expertise in RF propagation, indoor multipath, path-loss modeling, RTT distance estimation, and Channel State Information (CSI) extraction.
Proficiency in signal processing techniques including filtering, phase unwrapping, Kalman/EMA smoothing, noise modeling, and time-series feature extraction.
Advanced skills in Python and Matlab using libraries such as NumPy, SciPy, Pandas, and scikit-learn.
Hands-on experience with clustering, probabilistic modeling, similarity metrics, and lightweight ML classification or regression.
Experience deploying algorithms to real-time, enterprise-scale systems with tight latency constraints.
Knowledge of indoor positioning systems (IPS), robotics navigation, or embedded/mobile sensor pipelines.
Familiarity with 802.11mc/802.11az, BLE 5.x, and sensor-fusion frameworks.
Radar
Lidar
Camera
Python
Matlab
HIL
SIL
JAMA
DNG
Systems Engineering
Requirements Management
Functional Safety
Simulation
Data Analysis
Vehicle Integration