Senior Research Scientist - Network Systems

Peraton•Basking Ridge, NJ

9d•$146,000 - $234,000

About The Position

Peraton Labs conducts research and develops innovative capabilities for government agencies, utilities, and commercial customers. Its cybersecurity and network research supports mission-critical systems and national cyber infrastructure, and improves spectrum and system resource use through areas including computer network defense, secure-by-design, AI/ML, and deterministic and stochastic optimization. Research areas include networking and wireless technologies such as 5G and non-terrestrial networks; cybersecurity; resilient networking; software-defined networking for cyber defense and deception; and artificial intelligence. The Senior Research Scientist will apply expertise spanning RF communications and signal processing, machine learning, data analytics, and software development. The position calls for a record of work or publications across both RF/signal processing and machine learning, and the ability to take prototypes from concept through demonstration and communicate technical findings to stakeholders.

Requirements

Bachelor’s degree in Electrical Engineering, Computer Engineering, Computer Science, Applied Mathematics, or a related discipline, plus at least 12 years of experience; alternatively, at least 10 years with an MS/MA or 7 years with a Ph.D.
Strong foundation in RF communications theory, including modulation/demodulation, propagation, link budgets, and spectrum analysis.
Hands-on digital signal processing experience, including 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.
Proficiency designing and implementing supervised, unsupervised, deep-learning, and reinforcement-learning algorithms, with emphasis on signal/time-series data.
Experience building end-to-end ML pipelines covering data ingestion, preprocessing, feature engineering, training, validation, deployment, and monitoring.
Knowledge of CNN, RNN/LSTM, transformer, autoencoder, and GAN architectures, and applied experience selecting models for RF/signal and data-analytics problems.
Strong data analytics skills, including exploratory analysis, statistical modeling, visualization, and analysis of large or complex datasets.
Hands-on experience with PyTorch, TensorFlow/Keras, scikit-learn, and data tools such as NumPy, pandas, and Spark.
Experience with MLOps practices, including version control, Docker, CI/CD, experiment tracking tools such as MLflow or W&B, and model serving.
Strong programming skills in Python and C/C++.
Experience using Git, Linux development environments, and scripting.
Demonstrated project or publication record spanning both RF/signal processing and machine-learning domains.
Clear technical communication skills, including translating algorithms into implementation and results for stakeholders and writing technical papers and reports.
Self-directed approach and experience taking prototypes from concept through demonstration.
U.S. citizenship.

Nice To Haves

MATLAB experience.
Experience applying ML to RF problems such as signal classification, spectrum sensing, RFI mitigation, cognitive radio, direction finding, or emitter identification.
Background in DoD or IC programs, electronic warfare, SIGINT, or radar systems.
Active security clearance or ability to obtain one.

Responsibilities

Design and implement machine-learning algorithms for signal and time-series data.
Build end-to-end machine-learning pipelines, from data ingestion and preprocessing through deployment and monitoring.
Analyze large or complex datasets using exploratory analysis, statistical modeling, and visualization, and derive actionable insights.
Apply signal-processing and compressive-sensing methods to RF communications problems.
Use MLOps practices for version control, containerization, CI/CD, experiment tracking, and model serving.
Take prototypes from concept through demonstration.
Communicate technical algorithms, implementation details, and results to stakeholders, and write technical papers and reports.