RESEARCH COMPUTER SCIENTIST - RESEARCH ENGINEER - Artificial Intelligence / Machine Learning - Warne

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.

While professional experience and qualifications are key for this role, make sure to check you have the preferable soft skills before applying if required.

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). xhyhwjd

A valid/clear driver's license is required.