Senior Research Scientist - Network Systems

Peraton; Fort Meade (MD); On-site; USD 150,000 - 210,000; Full time

Job summary

Peraton is seeking a senior RF/ML engineer to advance research in RF communications, signal processing, and machine learning at Peraton Labs. The role emphasizes end-to-end ML solutions for RF/time-series data, applying advanced architectures and scalable data workflows.

You will contribute to projects spanning RF signal processing, ML model development, and data analytics, collaborating with a team to translate algorithms into deployable insights and demonstrable performance improvements.

Qualifications

• Strong foundation in RF communications theory, including modulation/demodulation, propagation, link budgets, and spectrum analysis.

Skills

RF theory

DSP experience

Compressive sensing

ML algorithms

ML pipelines

ML architectures

PyTorch

TensorFlow

scikit-learn

NumPy/Pandas

Docker

Git

Education

BS in EE/CE/CS/Applied Math

MS/MA degree

PhD

Tools

Python

C/C++

MATLAB

Git

Linux

Job description

Required Job Skills

RF Communications & Signal Processing

• Strong foundation in RF communications theory, including modulation/demodulation, propagation, link budgets, and spectrum analysis


• Hands-on experience with digital signal processing (DSP) — filtering, FFT/spectral analysis, detection, estimation, and adaptive algorithms


• Demonstrated expertise in compressive sensing, including sparse signal recovery, L1/L2 optimization, sensing matrix design, and sub-Nyquist sampling techniques

Machine Learning & Data Applications

• Proficiency designing and implementing ML algorithms (supervised, unsupervised, deep learning, reinforcement learning) with emphasis on signal/time-series data


• Experience building end-to-end ML pipelines — data ingestion, preprocessing, feature engineering, training, validation, deployment, and monitoring


• Knowledge of ML architectures including CNNs, RNNs/LSTMs, transformers, autoencoders, and GANs, with applied experience selecting the right model for RF/signal and data analytic problems


• Strong data analytics skills: exploratory analysis, statistical modeling, visualization, and deriving actionable insight from large/complex datasets


• Hands-on with frameworks such as PyTorch, TensorFlow/Keras, scikit-learn, and data tooling (NumPy, pandas, Spark)


• Experience with MLOps practices — version control, containerization (Docker), CI/CD, experiment tracking (MLflow, W&B), and model serving

Programming & Tools

• Strong programming skills in Python and C/C++; MATLAB a plus


• Version control (Git), Linux development environment, and scripting proficiency

Education & Experience & Citizenship

• BS in Electrical Engineering, Computer Engineering, Computer Science, Applied Mathematics, or related discipline and a minimum of 12 years of experience; Minimum of 10 years with MS/MA; Minimum of 7 years with Ph.D.


• Demonstrated project or publication record spanning both RF/signal processing and machine learning domains


• US Citizenship

Preferred / Nice-to-Have

• Experience applying ML to RF problems: signal classification, spectrum sensing, RFI mitigation, cognitive radio, direction finding, or emitter identification


• Background in DoD / IC programs, EW, SIGINT, or radar systems


• Active security clearance (or ability to get one)

Soft Skills

• Clear technical communication — able to translate algorithms to implementation and results to stakeholders, and write technical papers and reports


• Self-directed, with experience taking prototypes from concept through demonstration

Peraton Labs delivers innovative solutions and revolutionary new capabilities to solve the most difficult and complex challenges for government agencies, utilities, and commercial customers. With a distinguished heritage tracing back to Bell Labs, Bellcore, and Telcordia, our experts pave the way. Peraton Labs' cybersecurity and network research protects mission-critical systems and national cyber infrastructure and efficiently utilizes spectrum and system resources by employing technologies in the areas of computer network defense, secure-by-design, AI/ML and deterministic and stochastic optimization.

Our projects span several areas of interest. Our core competencies include (but are not limited to):

• Networking and wireless research including 5G and non-terrestrial networks


• Cyber Security including formal methods, side channel analysis, high-fidelity emulation of cyber physical systems (CPS)


• Resilient networking in support of cyber mission goals


• Software-defined networking for cyber defense and deception


• Artificial intelligence (reasoning, probabilistic inference, machine learning)