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 of their mathematical foundations and common failure modes is more important than operational experience. - 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.). Company Description Global InfoTek Inc. has an award-winning track record of designing, developing, and deploying best-of-breed technologies that address the nation's pressing cyber and advanced technology needs. GITI has rapidly merged pioneering technologies, operational effectiveness, and best business practices for over two decades.

• Work on high-impact problems at the intersection of LLM research, large-scale ML systems, and real-world e-commerce applications. • Choose to work in areas like query understanding, search relevance and ranking, generative recommendations, LLM evaluation, and more.

• Translate cutting-edge research into production-ready machine learning systems • Design, build, and deploy end-to-end ML models and pipelines • Develop and optimize models for image and video processing • Own the full ML lifecycle: experimentation, training/fine-tuning, evaluation, and deployment • Build low-latency, real-time inference systems and scalable ML infrastructure • Rapidly prototype using open-source models and adapt them for product needs • Conduct experiments, analyze results, and iterate to improve performance • Collaborate with researchers and cross-functional teams (product, engineering, design) to deliver ML solutions at scale • Stay current with advancements in machine learning and apply them to continuously improve products

Role Description We are looking for a Machine Learning Engineer with strong statistical foundations and hands-on modeling experience to join the team. You will assist in building, maintaining, and improving predictive models across a range of business domains — customer retention, network performance, marketing, sales, and others as needs evolve. You will develop features from complex multi-source data and help maintain inherited models built by external partners. You will contribute to all phases of the modeling lifecycle, from data exploration through model delivery, under the direction of the team manager. This position will report directly to the team manager and work alongside data engineers and solutions architects. It can be filled remotely anywhere within the country. What You Will Do - Build and evaluate predictive models (logistic regression, XGBoost, ensemble methods) across customer retention, network, marketing, sales, and other business domains. - Engineer features from complex, multi-source enterprise data (billing systems, call center logs, CRM, network data) in Snowflake and Oracle. - Profile and investigate data quality issues — identify leakage, missingness patterns, join inconsistencies, and source-of-truth conflicts. - Maintain and improve inherited production models, including models built in Snowpark by external partners. - Perform SHAP-based model interpretability analysis and translate results into business-actionable insights. - Design and execute customer segmentation using clustering techniques on model outputs. - Write clear, thorough documentation of model logic, feature rationale, data assumptions, and known limitations. - Collaborate with the team to define target variables, population filters, and prediction windows grounded in statistical reasoning. Qualifications - 2–3 years of experience in a data science or applied statistics role (less experience considered for strong candidates). - Strong foundation in statistics: hypothesis testing, regression, classification, probability, bias-variance tradeoffs. - Proficiency in Python for data science (Pandas, scikit-learn, NumPy, Matplotlib/Seaborn). - Strong SQL skills, particularly with Snowflake or similar cloud data warehouses. - Experience with feature engineering from real-world, imperfect enterprise data — not just clean Kaggle datasets. - Ability to work independently and manage your own priorities with minimal oversight. - Clear written and verbal communication — you can explain a modeling decision to a non-technical stakeholder and document your work so others can follow it. Even Better - Experience with Snowpark (Python or SQL). - Exposure to Azure ML or similar cloud ML platforms. - Familiarity with MLOps concepts (model versioning, pipeline automation, drift monitoring). - Telecom or subscription-based industry experience. - Experience inheriting and maintaining models built by others. - Familiarity with Git-based workflows and version control for data science artifacts. Benefits - Medical, Dental, Vision Insurance Plans. - 401K Plan. - Health & Flexible Savings Account. - Life and AD&D, Spousal Life, Child Life Insurance Plans. - Educational Assistance Plan.