Research Scientist - RF Machine Learning

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, Tensor Flow, 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

• 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, Blade
  
  RF, 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 Num Py, Sci Py, Pandas, cu Signal, 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

Peraton Labs is seeking a poly cleared Senior Research Scientist to support cleared research and development efforts for a Maryland-based IC customer. This role will focus 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.

You’ll work on mission-focused R&D efforts that move from research concepts to working prototypes and operationally relevant capabilities. You will collaborate with researchers, software engineers, signal processing experts, and customer stakeholders to solve complex problems involving RF sensing, signal characterization, waveform analysis, spectrum awareness, and machine learning-enabled wireless…