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. 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 Py Torch or Tensor Flow, 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.