Research Scientist - RF Machine Learning

Overview Peraton Labs is seeking a poly‑cleared Senior Research Scientist to support cleared research and development efforts for a Maryland‑based IC customer. The role focuses on leading the design, development, prototyping, and evaluation of RF Machine Learning algorithms and signal processing techniques for advanced wireless, spectrum, cyber, and communications research. Full‑time on‑site work is required at a customer site near College Park, MD. Responsibilities Lead the design, development, prototyping, and evaluation of RF/ML algorithms for wireless, spectrum, and communications applications. Research and implement machine learning approaches for RF signal detection, classification, characterization, anomaly detection, emitter identification, spectrum sensing, or waveform analysis. Develop and evaluate algorithms using modern machine learning frameworks such as PyTorch, TensorFlow, Keras, scikit‑learn, JAX, or similar tools. Apply strong digital signal processing and RF domain knowledge to develop, train, test, and validate models against real‑world or simulated RF data. Design data collection, labeling, preprocessing, feature extraction, training, evaluation, and experimentation workflows for RFML research. Develop software prototypes using Python, C/C++, MATLAB, GNU Radio, or similar tools. Analyze RF signals, wireless protocol behavior, modulation characteristics, channel effects, interference, noise, and system performance. Work with RF datasets, signal captures, IQ data, SDR platforms, and lab or field‑collected spectrum data. Support integration of RFML capabilities into larger research prototypes, testbeds, cyber experimentation platforms, or operationally relevant systems. Communicate research findings, technical approaches, experiment results, and prototype capabilities through customer briefings, technical reports, whitepapers, and publications. Provide technical leadership, mentor junior researchers or engineers, and help shape future RFML research direction. Qualifications Minimum Qualifications Minimum of 6+ years of experience with a Bachelor’s degree, 4+ years of experience with a Master’s degree, or 2+ years of experience with a Ph.D. in Electrical Engineering, Computer Engineering, Computer Science, Applied Mathematics, Physics, or a related discipline. In lieu of a Bachelors, an additional 4 years of experience is required for a total of 10+ years. Strong background in Radio frequency Machine Learning, digital signal processing, wireless communications, or RF systems research. Experience designing, developing, training, testing, or evaluating machine learning models for RF, wireless, spectrum, signal processing, or communications applications. Experience with modern machine learning frameworks such as PyTorch, TensorFlow, Keras, scikit‑learn, or similar tools. Strong experience programming in Python and at least one additional language such as C/C++, Java, or similar. Experience working with RF data, signal captures, IQ samples, simulated waveforms, or real‑world wireless datasets. Experience working in Linux‑based dev environments. Ability to develop, test, troubleshoot, document, and demonstrate research prototypes. Strong written and verbal communication skills, including the ability to present technical concepts and research results to technical stakeholders. US Citizenship is a requirement for this position. This position requires an active/current TS/SCI w/ Polygraph. Desired Additional Qualifications Advanced degree in Electrical Engineering, Computer Engineering, Computer Science, Applied Mathematics, Physics, or a related technical field is preferred. Demonstrated history of research in Machine Learning and RF spectrum domains, including publications, prototypes, proposals, patents, technical reports, or customer‑facing research briefings. Experience with RFML applications such as signal classification, modulation recognition, emitter identification, spectrum sensing, anomaly detection, interference detection, protocol inference, or RF fingerprinting. Experience with SDR platforms such as Ettus USRP, HackRF, BladeRF, LimeSDR, or similar hardware. Familiarity with SDR software and RF development tools such as GNU Radio, UHD/USRP, MATLAB, Simulink or similar tools. Experience with wireless systems or protocols such as LTE, 5G, Wi‑Fi, SATCOM, MANET, tactical radio systems, mesh networks, or custom waveform environments. Experience with RF test equipment such as spectrum analyzers, signal generators, oscilloscopes, vector signal analyzers, channel emulators, or RF front‑end equipment. Experience with deep learning approaches for signal processing, including CNNs, RNNs, transformers, autoencoders, contrastive learning, self‑supervised learning, or generative models. Experience with data engineering for RFML, including dataset generation, augmentation, labeling, synthetic data, simulation, model evaluation, and experiment tracking. Experience with tools such as NumPy, SciPy, Pandas, cuSignal, CUDA, MLflow, Weights & Biases, DVC, or similar tools. Experience integration ML models into deployable prototypes, edge systems, containers, testbeds, or cyber/radio experimentation environments. Experience with RF cyber research, wireless security, electronic warfare, spectrum operations, protocol reverse engineering, or adversarial ML. Ability to serve as a technical lead, task lead, or principal investigator on DoD/IC research efforts. Additional Information Target Salary Range: $135,000 – $216,000. This represents the typical salary range for this position. 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