RF Modeling Manager

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.
• Fuse intelligence by combining Wi-Fi RSSI, BLE RSSI, RTT timestamps, and IMU patterns to produce hybrid models that outperform single-sensor approaches.
• Experiment relentlessly by evaluating accuracy using ground-truth traces, running controlled experiments, tuning hyperparameters, and improving model confidence scoring.
• Operationalize intelligence by deploying models into real-time scoring pipelines, collaborating with cloud engineering to ensure sub-100ms inference and large-scale reliability.
• Collaborate closely with firmware, data, RF, QA, and product teams.
• Mentor peers in algorithmic reasoning and modeling excellence.

Qualifications

• 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.

Target Base Salary Range

Range: $140,000 - $170,000 USD

Basic Requirements

• Master’s 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.
• 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.