Job description

• 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. • 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. • 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. Considerations: 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. Applicant must be a U.S. citizen.

Requirements

Must-haves

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.
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.
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.
A valid/clear driver's license is required.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learning

Platform: swri
Effort: medium