Role Description GITI is seeking a Senior AI/ML Engineer to support an R&D program focused on passive RF emitter identification and network analysis from real-time sensor data streams. The Senior AI/ML Engineer designs, builds, and validates machine learning models for RF emitter identification, conducts hands-on exploratory data analysis on NDF (Network Description File) sensor datasets, and implements ML data pipelines that operate on constrained tactical edge hardware. Working under the direction of the Principal AI/ML Engineer and program technical lead, the candidate collaborates closely with research scientists and software engineers to translate analytical findings into reproducible, well-documented ML experiments and pipeline components. The role requires strong Python and deep learning skills, comfort with real-world noisy sensor data, and the ability to work in air-gapped Linux environments without cloud infrastructure or GPU acceleration. Responsibilities - Design, build, and validate machine learning models for RF emitter identification, including feature engineering from sensor data, training pipeline development, model evaluation, and iterative refinement based on results. - Conduct hands-on exploratory data analysis on RF sensor datasets using Python and Jupyter notebooks, writing and running analytical code, characterizing feature distributions, identifying data quality issues, and producing documented findings. - Implement and maintain ML data pipelines, ingesting NDF sensor streams, applying rollup and preprocessing logic, constructing training datasets, and ensuring pipeline correctness on constrained edge hardware with no cloud dependency. - Collaborate with the technical lead and Principal AI/ML Engineer to investigate RF sensor data quality, attribution reliability, and feature behavior under contention, writing code to characterize error sources, validate assumptions, and reproduce findings. - Produce clear technical documentation of experiments, model configurations, and results, maintaining reproducibility through disciplined versioning, and contributing to monthly status reports and team knowledge sharing. Qualifications - Bachelor’s or Master’s (or equivalent) with 5–7 years of hands-on applied experience. Requirements - 5+ years of hands-on applied experience in machine learning, data science, or RF signal processing. - Demonstrated proficiency in Python for ML and data science work — PyTorch or TensorFlow for model development, Pandas/NumPy for data manipulation, and scikit-learn or similar for evaluation and baseline modeling. - Hands-on experience designing, training, and evaluating deep learning models, particularly metric learning, Siamese networks, or other similarity-learning architectures on real-world, noisy, imbalanced datasets. - Practical experience handling real-world data quality problems — missing values, label noise, class imbalance, systematic bias, and sensor artifacts — and the ability to diagnose and address them without discarding valid data. - Ability to develop and run ML pipelines on Linux-based systems without cloud infrastructure or GPU acceleration, optimizing for CPU-only inference and multi-threaded data processing on resource-constrained x86 hardware. Desired Skills - Familiarity with RF signal characteristics, passive receiver phenomenology, and sensor data interpretation, including awareness of processing artifacts, attribution ambiguities, and measurement limits common in signals intelligence datasets. - Hands-on experience applying machine learning, particularly metric learning, deep learning networks, or similarity-learning architectures to RF or time-series signal data, including feature engineering, training pipeline development, and model validation. - Exposure to TDMA network protocols or military datalink systems, and interest in learning the signal processing challenges of dense, contested electromagnetic environments. - Familiarity with direction-finding, time-difference-of-arrival (TDOA), or related passive geolocation concepts, understanding their mathematical foundations and common failure modes. - Experience with binary serialization formats (FlatBuffers, Protocol Buffers) and high-throughput sensor data pipelines operating in near-real-time on resource-constrained hardware. - Background in statistical signal processing — error ellipses, bearing estimation uncertainty, feature reliability under noise — with the ability to distinguish statistically significant findings from artifacts of small sample size or improper normalization. Relevant Certifications - Certifications in machine learning, data science, or related technical fields (e.g., TensorFlow Developer Certificate; PyTorch Certified Associate; AWS Certified Machine Learning — Specialty; Microsoft Certified: Azure AI Engineer Associate; Certified Analytics Professional (CAP); etc.).