RF Modeling Manager

Company Overview

At Motorola Solutions we believe that everything starts with our people. Were 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 thats 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.

Department Overview

Job Description

Responsibilities:

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 time-series modeling to transform raw RF and IMU data into high-quality features.

Fuse Intelligence: Combine Wi-Fi RSSI 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

Qualifications:

Masters 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.

RF & Sensors: Solid understanding of RF propagation indoor multipath path-loss modeling and RTT distance estimation.

Signal Processing Specialist: Experience with filtering Kalman/EMA smoothing noise modeling and time-series feature extraction.

Data Wrangler: Strong Matlab Python skills (NumPy SciPy Pandas scikit-learn) and experience working with Wi-Fi RSSI BLE RSSI RTT/FTM IMU datasets.

AI/ML Engineer: Hands-on experience with clustering probabilistic modeling similarity metrics and lightweight ML classification/regression.

Production Mindset: Experience deploying algorithms to real-time enterprise-scale systems with tight latency constraints.

Algorithm Scientist Thinking: Able to analyze noisy data design robust models validate hypotheses and convert prototypes into production-ready logic.

Target Base Salary Range: $140000 - $170000 USD
Consistent with Motorola Solutions values and applicable law we provide the following information to promote pay transparency and equity. Pay within this range varies and depends on job-related knowledge skills and experience. The actual offer will be based on the individual candidate.

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Basic Requirements

Masters degree with 6 years OR a PhD with 4 years of professional experience in AI/ML Electrical Engineering CS Applied Math Robotics or a related technical discipline.

Travel Requirements

Relocation Provided

Position Type

Referral Payment Plan

Our :

• Incentive Bonus Plans

• Medical Dental Visionbenefits

• 401K with Company Match

• 10 Paid Holidays

• GenerousPaidTime Off Packages

• Employee Stock Purchase Plan

• PaidParental & Family Leave

• and more!

EEO Statement

Motorola Solutions is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race color religion or belief sex sexual orientation gender identity national origin disability veteran status or any other legally-protected characteristic.

We are proud of our people-first and community-focused culture empowering every Motorolan to be their most authentic self and to do their best work to deliver on the promise of a safer world. If youd like to join our team but feel that you dont quite meet all of the preferred skills wed still love to hear why you think youd be a great addition to our team.

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