Senior Indoor Localization & Sensor Fusion Engineer

Motorola Solutions; Dallas (TX); On-site; USD 140,000 - 160,000; Full time

Benefits offered by this job

Incentive Bonus Plans

Medical, Dental, Vision benefits

401K with Company Match

10 Paid Holidays

Generous Paid Time Off

Employee Stock Purchase Plan

Paid Parental & Family Leave

Job summary

Motorola Solutions is seeking a Principal Staff Engineer to lead indoor location intelligence and sensor fusion efforts. You will design AI/ML models that fuse Wi-Fi, CSI, RTT, BLE, and IMU data to deliver real-time positioning at scale, with sub-100ms inference targets.

You'll work across RF, signal processing, and software engineering teams, optimize path-loss and multipath models, and deploy production-ready pipelines in cloud/edge environments while mentoring peers in advanced modeling

Qualifications

• Master's degree with 8+ years in AI/ML or related field (PhD preferred).
• Strong RF propagation and CSI analysis knowledge.
• Experience deploying real-time, enterprise-scale systems.
• Proficiency with Python and MATLAB.

Responsibilities

• Model and innovate AI-driven indoor localization.
• Refine RF propagation and path-loss models.
• Process RF/CSI/IMU data into features.
• Fuse Wi-Fi RSSI, CSI, BLE RSSI, RTT, and IMU data.
• Tune models and assess accuracy with ground-truth traces.
• Deploy real-time scoring pipelines with sub-100ms latency.
• Collaborate with firmware, data, RF, QA, and product teams.

Skills

Indoor localization

RF modeling

Signal processing

Python

MATLAB

AI/ML

Real-time systems

Cloud deployment

Education

Master's degree +8 years

PhD preferred

Tools

Wi-Fi CSI

RTT/FTM

BLE RSSI

Antenna arrays

Job description

Motorola Solutions is seeking a Principal Staff Engineer to lead indoor location intelligence and sensor fusion efforts. You will design AI/ML models that fuse Wi-Fi, CSI, RTT, BLE, and IMU data to deliver real-time positioning at scale, with sub-100ms inference targets.

You'll work across RF, signal processing, and software engineering teams, optimize path-loss and multipath models, and deploy production-ready pipelines in cloud/edge environments while mentoring peers in advanced modeling