Who We Are:The Spectrum Innovation section within the Defense & Intelligence Solutions Division specializes in innovating the next generation of Electronic Warfare to benefit warfighters around the globe.
Objectives of this Role: - Architect mission-tailored neural network models that address critical electronic warfare challenges, including cognitive EW, real-time RF signal classification, spectrum agility, and automated electronic attack/protection techniques.
- Drive synthetic and measured data generation pipelines to ensure robust, validated, and representative RF and EW datasets are established for training, fine-tuning, and testing mission-critical algorithms.
- Conduct rigorous technical assessments of emerging commercial and academic AI/ML frameworks to advise clients and program leadership on architectural trade-offs, feasibility, and edge deployment viability.
- Serve as a primary technical advisor and liaison in sprint reviews, milestone gates, and customer-facing briefings to clearly communicate technical risks, algorithmic performance metrics, and project status.
Daily and Monthly Responsibilities: - Design, train, optimize, and profile custom deep learning architectures (e.g., CNNs, RNNs/Transformers, graph neural networks, or neuromorphic models) for RF signal characterization and EW countermeasure development.
- Develop, preprocess, and validate multi-sensor RF and EW datasets through synthetic generation tools, hardware-in-the-loop (HWIL) lab collections, and signal processing transformations.
- Conduct systematic benchmarking, technical reviews, and stress testing of third-party or client-provided ML architectures to deliver comprehensive evaluation reports and recommendations.
- Prepare and deliver progress briefings, technical demonstrations, and sprint milestone updates to cross-functional engineering teams, management, and external customer sponsors.
- Collaborate with systems and embedded software engineers to containerize models and ensure architectural compatibility with modular frameworks and edge compute constraints.
Requirements: - Requires a Bachelors or a Masters degree in Electrical Engineering, Computer Engineering, Computer Science, Data Science or related technical degree field with research and/or experience focused on machine learning and signal processing.
- 4-8 years: Hands-on experience architecting, training, and deploying deep learning models using modern frameworks such as PyTorch or TensorFlow, with a demonstrated focus on complex or non-traditional data types.
- 3 years: Practical experience building end-to-end data pipelines, including the generation of synthetic data, augmentation of sparse datasets, and formal data quality/labeling validation for mission-critical systems.
- 3 years: Professional experience in technical communication, including authoring formal evaluation reports, presenting at customer design reviews, and leading technical discussions across multidisciplinary teams.
- 1 years: Demonstrated experience applying machine learning techniques to digital signal processing (DSP), radio frequency (RF) signals, communications, or electronic warfare domains (e.g., modulation recognition, spectrum sensing, emitter identification).
- A valid/clear driver's license is required.
Special Requirements:Work assignment is in Warner Robins, Georgia. Applicant selected will be subject to a government security investigation and must meet eligibility requirements for access to classified information.
Job Locations: Warner Robins, Georgia Or Dayton, OhioFor more information about this division, visit the Defense & Intelligence Solutions home page.
For benefits information at our San Antonio location, click here .
For benefits information at all other locations, click here .
An Equal Employment Opportunity Employer: race, color, religion, sex, national origin, disability, and veteran status.
