Global InfoTek, Inc.

• Support Cyber Operations Research and Development as the technical lead for production software development • Own the architecture, implementation, and delivery of the production pipeline • Lead a team of Senior Software Engineers in support of real world cyber operations • Establish and maintain disciplined software engineering practices • Collaborate with the program technical lead to translate research findings into production pipeline components

Role Description GITI is seeking a Principal Scientist to serve as the senior technical authority on an R&D program focused on passive RF emitter identification and network analysis from real-time sensor data streams. The Principal Scientist leads independent, hands-on analysis of NDF (Network Description File) sensor datasets, provides technical direction across parallel research threads, and serves as the primary technical advisor to the government sponsor. The role spans the full research lifecycle: - Formulating hypotheses - Writing and executing analytical code in Python and Jupyter notebooks - Interpreting and validating results - Communicating findings to both technical peers and non-specialist stakeholders This is a deeply technical, hands-on position — the Principal Scientist conducts analysis directly and does not delegate technical work as a substitute for personal proficiency. The candidate will work within a small, distributed team operating in air-gapped Linux environments on resource-constrained tactical edge hardware, with no cloud computing. Qualifications - 10+ years of hands-on applied R&D experience in RF systems, signals intelligence, electronic warfare, or related domains. - Proven ability to quickly acquire domain knowledge; specifically in the areas of wireless digital communications and military techniques, tactics, and procedures. - Demonstrated ability to independently develop and execute data analyses in Python or equivalent tools on real sensor datasets; must be capable of writing production-quality analytical code, not merely directing others to do so. - Experience addressing common problems with large quantities of real-world data, such as imputation, noise, bias, and errors. - Track record of working effectively on constrained-hardware edge systems — no cloud, no discrete GPU — with attention to computational efficiency and multi-core, multi-thread performance on x86 platforms. Requirements - Expert-level career professional recognized as a technical authority in RF systems, signals intelligence, or a closely related applied domain. - Exercises broad independent judgment in defining research approach, evaluating methods, and interpreting results. - Operates with minimal supervision; accountable for the scientific integrity and practical relevance of program research outputs. - Advanced degree (MS or PhD) with 10+ years of hands-on applied R&D experience. Desired Skills - Deep familiarity with RF signal characteristics, sensor phenomenology, and the interpretation of passive receiver data — including recognition of processing artifacts, attribution ambiguities, and the limits of sensor-derived measurements. - 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. - Familiarity with TDMA network protocols, emitter identification techniques (CID/PID), and the signal processing challenges of dense, contested electromagnetic environments. - Experience with interferometric direction-finding, TDOA geolocation, or related passive geolocation methods, including practical knowledge of their failure modes and accuracy limitations. - 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 - Professional certifications in data science, signal processing, or related technical fields. - Advanced academic credentials (PhD, MS) in a relevant quantitative discipline are strongly preferred and may substitute for certifications.

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.

Role Description GITI is seeking a Lead RF Software Engineer to support Cyber Operations Research and Development on passive RF emitter identification and network analysis from real-time sensor data streams. The candidate will implement, test, and maintain components of production software pipeline — a stream ingestion, rollup, and post-processing system operating on NDF (Network Description File) data produced by TDMA network sensors in dense, contested RF environments. Working under the direction of the Principal Engineer and the Technical Lead, the Lead RF Software Engineer supports Cyber Operations by contributing to pipeline development across a range of functional areas including: - Stream processing - Database integration - Display and reporting tools - Simulation infrastructure - CI/CD tooling The role requires strong Python skills, comfort with air-gapped Linux environments, and the ability to work independently on well-defined components with minimal supervision in support of real-world cyber operations. Responsibilities - Implement, test, and maintain assigned pipeline components including stream ingestion, rollup processing, database write, and batch post-processing modules in support of real-world cyber operations. - Develop and maintain browser-based visualization and reporting tools (track plots, waterfall displays, SmartBook report generation) that consume pipeline database output. - Implement and maintain stream simulation infrastructure, including TDMA network mission log replay and stream generation at controllable rates for pipeline testing. - Develop lightweight TNS simulator components: emitter and receiver models capable of following track plots and emitting in accordance with a network description. - Contribute to database integration work on tactical-box-spec hardware, including MySQL schema design, query optimization, and performance benchmarking. - Write comprehensive unit and integration tests for assigned components; implement and maintain CI/CD pipelines using GitLab to ensure functionality on hardware or in cloud environment. - Identify and report performance bottlenecks in Python pipeline components; assist with porting mature components to Rust or C as directed. - Perform basic Linux system administration on remote servers including package management, user configuration, and environment setup. - Manage source code using GitLab; follow disciplined versioning, branching, and code review practices as established by the Principal Engineer. - Produce clear technical documentation for implemented components including interface specifications, configuration guides, and test procedures. - Participate in periodic technical check-ins with the program technical lead; share findings and flag blockers promptly. Qualifications - Bachelor's (or equivalent) with 7-10 years of experience, or a Master's with 5-7 years of experience. Requirements - Strong proficiency in Python, with demonstrated experience in data processing pipelines, stream ingestion, or ETL development. - Proficiency with Python data science libraries including NumPy, Pandas (or Polars), and scikit-learn. - Experience with relational database development using MySQL, PostgreSQL, or SQLite, including schema design and query optimization. - Experience parsing or generating binary serialization formats (FlatBuffers, Protocol Buffers, or equivalent). - Ability to develop, test, and debug on remote Linux servers via SSH using command-line tools and a modern IDE. - Solid Linux operating system fundamentals including file system management, process control, and basic security hardening (Ubuntu). - Proficient in software engineering practices including Git/GitLab version control, unit testing, and CI/CD pipeline usage. - Experience developing browser-based data visualization or reporting tools, or demonstrated ability to learn React/D3-based tooling on the job. - Strong written and oral communication skills; ability to produce clear technical documentation for engineering audiences. - Ability to work independently on assigned components with minimal supervision in a small, distributed team. Desired Skills - Experience with TNS (Target Network System) sensor data formats and NDF ICD specifications. - Familiarity with TDMA network protocols, time-division access architectures, and passive RF signal processing concepts. - Experience with lightweight stream or message queue architectures (ZeroMQ, RabbitMQ, or equivalent). - Experience with Rust or Go for systems-level or performance-critical development on Linux. - Experience with Polars or DuckDB for high-performance analytical workloads. - Experience with performance profiling and optimization of Python pipelines on resource-constrained x86 hardware. - Experience with LLM-assisted software development tools (e.g., Claude Code, GitHub Copilot, JetBrains AI Assistant, or equivalent); demonstrated ability to use AI tools productively for code generation, refactoring, and test case development while maintaining engineering judgment and code quality standards. - Familiarity with AI/ML libraries (PyTorch, TensorFlow); ability to integrate trained model inference into a pipeline without requiring deep ML expertise. - Experience with Jupyter Notebooks and research enclave environments; ability to read and adapt research prototype code. - Experience with simulation or synthetic data generation for pipeline testing purposes. - Familiarity with Apache data science tools such as Spark or Dask for large-scale data processing. Relevant Certifications - Certifications in software engineering, computer science, or related fields (e.g., Certified Software Development Professional (CSDP); Certified Scrum Developer (CSD); Red Hat Certified Enterprise Application Developer; Certified Secure Software Lifecycle Professional (CSSLP); C++ Certified Associate Programmer (CPA); Professional Software Developer Certification (PSD); etc.)

• 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

• Own the architecture and implementation of the production software pipeline, including stream ingestion, rollup, database write, and batch post-processing components • Lead a team of Senior Software Engineers in support of real world cyber operations; assign work, conduct code reviews, enforce quality standards, and provide technical mentorship • Establish and maintain disciplined software engineering practices: versioning, CI/CD pipelines, unit and integration testing, and documentation standards • Design and evaluate database and storage architecture for the tactical system and research enclave environments • Collaborate with the program technical lead to translate research findings and batch optimization algorithms into production pipeline components • Evaluate and benchmark Python pipeline performance on tactical-box-spec hardware; identify bottlenecks and lead porting of mature components to Rust or C for edge deployment • Manage and coordinate the tactical system VM environment and stream simulation infrastructure; ensure research VM is not disrupted by development activity • Define and enforce stream interface contracts between the ingestion layer, database, and downstream consumers • Evaluate emerging technologies (e.g., DuckDB/Parquet, Polars, message queues) against program requirements and recommend adoption decisions to the technical lead • Maintain the program’s GitLab repository structure, branching strategy, and release management • Produce clear technical documentation including architecture decision records, interface specifications, and deployment guides • Support technical reviews and provide written inputs for sponsor deliverables as directed by the program technical lead

• Implement, test, and maintain assigned pipeline components including stream ingestion, rollup processing, database write, and batch post-processing modules in support of real world cyber operations • Develop and maintain browser-based visualization and reporting tools (track plots, waterfall displays, SmartBook report generation) that consume pipeline database output • Implement and maintain stream simulation infrastructure, including TDMA network mission log replay and stream generation at controllable rates for pipeline testing • Develop lightweight TNS simulator components: emitter and receiver models capable of following track plots and emitting in accordance with a network description • Contribute to database integration work on tactical-box-spec hardware, including MySQL schema design, query optimization, and performance benchmarking • Write comprehensive unit and integration tests for assigned components; implement and maintain CI/CD pipelines using GitLab to ensure functionality on hardware or in cloud environment • Identify and report performance bottlenecks in Python pipeline components; assist with porting mature components to Rust or C as directed • Perform basic Linux system administration on remote servers including package management, user configuration, and environment setup • Manage source code using GitLab; follow disciplined versioning, branching, and code review practices as established by the Principal Engineer • Produce clear technical documentation for implemented components including interface specifications, configuration guides, and test procedures • Participate in periodic technical check-ins with the program technical lead; share findings and flag blockers promptly

• Conduct independent, hands-on data analysis on RF sensor datasets using Python and Jupyter notebooks — formulating hypotheses, writing and running analytical code, interpreting results, and producing findings that directly advance program research objectives • Provide technical advice and research direction across a multidisciplinary team; define analytical objectives, review and validate technical outputs from AI/ML engineers and software developers, and ensure coherence across parallel research threads • Serve as primary technical advisor to the government sponsor: translate operational requirements into research objectives, communicate findings clearly to non-specialist stakeholders, and maintain program alignment with sponsor priorities through written reports and technical presentations • Design and execute analytical investigations into RF sensor data quality, emitter behavior, and attribution reliability — including characterizing error sources, identifying systematic artifacts, and developing methods to distinguish real physical signatures from sensor or processing artifacts • Produce technical documentation — working notes, research findings, monthly status reports, and briefing materials — that accurately represent the scope and confidence level of analytical results • Expert-level career professional recognized as a technical authority in RF systems, signals intelligence, or a closely related applied domain. Exercises broad independent judgment in defining research approach, evaluating methods, and interpreting results. Operates with minimal supervision; accountable for the scientific integrity and practical relevance of program research outputs.

• Implement, test, and maintain assigned pipeline components • Develop and maintain browser-based visualization and reporting tools • Implement and maintain stream simulation infrastructure • Develop lightweight TNS simulator components • Contribute to database integration work • Write comprehensive unit and integration tests for assigned components • Perform basic Linux system administration on remote servers • Manage source code using GitLab • Produce clear technical documentation for implemented components • Participate in periodic technical check-ins with the program technical lead

Role Description GITI is seeking a Senior RF Software Engineer to support Cyber Operations Research and Development on passive RF emitter identification and network analysis from real-time sensor data streams. The candidate will implement, test, and maintain components of production software pipeline — a stream ingestion, rollup, and post-processing system operating on NDF (Network Description File) data produced by TDMA network sensors in dense, contested RF environments. Working under the direction of the Principal Engineer and the Technical Lead, the Senior RF Software Engineer supports Cyber Operations by contributing to pipeline development across a range of functional areas including: - Stream processing - Database integration - Display and reporting tools - Simulation infrastructure - CI/CD tooling The role requires strong Python skills, comfort with air-gapped Linux environments, and the ability to work independently on well-defined components with minimal supervision in support of real-world cyber operations. Qualifications - Bachelor’s (or equivalent) with 5–7 years of experience, or a Master’s with 3–5 years of experience. Requirements - Implement, test, and maintain assigned pipeline components including stream ingestion, rollup processing, database write, and batch post-processing modules in support of real-world cyber operations. - Develop and maintain browser-based visualization and reporting tools (track plots, waterfall displays, SmartBook report generation) that consume pipeline database output. - Implement and maintain stream simulation infrastructure, including TDMA network mission log replay and stream generation at controllable rates for pipeline testing. - Develop lightweight TNS simulator components: emitter and receiver models capable of following track plots and emitting in accordance with a network description. - Contribute to database integration work on tactical-box-spec hardware, including MySQL schema design, query optimization, and performance benchmarking. - Write comprehensive unit and integration tests for assigned components; implement and maintain CI/CD pipelines using GitLab to ensure functionality on hardware or in cloud environment. - Identify and report performance bottlenecks in Python pipeline components; assist with porting mature components to Rust or C as directed. - Perform basic Linux system administration on remote servers including package management, user configuration, and environment setup. - Manage source code using GitLab; follow disciplined versioning, branching, and code review practices as established by the Principal Engineer. - Produce clear technical documentation for implemented components including interface specifications, configuration guides, and test procedures. - Participate in periodic technical check-ins with the program technical lead; share findings and flag blockers promptly. Required Skills - Strong proficiency in Python, with demonstrated experience in data processing pipelines, stream ingestion, or ETL development. - Proficiency with Python data science libraries including NumPy, Pandas (or Polars), and scikit-learn. - Experience with relational database development using MySQL, PostgreSQL, or SQLite, including schema design and query optimization. - Experience parsing or generating binary serialization formats (FlatBuffers, Protocol Buffers, or equivalent). - Ability to develop, test, and debug on remote Linux servers via SSH using command-line tools and a modern IDE. - Solid Linux operating system fundamentals including file system management, process control, and basic security hardening (Ubuntu). - Proficient in software engineering practices including Git/GitLab version control, unit testing, and CI/CD pipeline usage. - Experience developing browser-based data visualization or reporting tools, or demonstrated ability to learn React/D3-based tooling on the job. - Strong written and oral communication skills; ability to produce clear technical documentation for engineering audiences. - Ability to work independently on assigned components with minimal supervision in a small, distributed team. Desired Skills - Experience with TNS (Target Network System) sensor data formats and NDF ICD specifications. - Familiarity with TDMA network protocols, time-division access architectures, and passive RF signal processing concepts. - Experience with lightweight stream or message queue architectures (ZeroMQ, RabbitMQ, or equivalent). - Experience with Rust or Go for systems-level or performance-critical development on Linux. - Experience with Polars or DuckDB for high-performance analytical workloads. - Experience with performance profiling and optimization of Python pipelines on resource-constrained x86 hardware. - Experience with LLM-assisted software development tools (e.g., Claude Code, GitHub Copilot, JetBrains AI Assistant, or equivalent); demonstrated ability to use AI tools productively for code generation, refactoring, and test case development while maintaining engineering judgment and code quality standards. - Familiarity with AI/ML libraries (PyTorch, TensorFlow); ability to integrate trained model inference into a pipeline without requiring deep ML expertise. - Experience with Jupyter Notebooks and research enclave environments; ability to read and adapt research prototype code. - Experience with simulation or synthetic data generation for pipeline testing purposes. - Familiarity with Apache data science tools such as Spark or Dask for large-scale data processing. Relevant Certifications - Certifications in software engineering, computer science, or related fields (e.g., Certified Software Development Professional (CSDP); Certified Scrum Developer (CSD); Red Hat Certified Enterprise Application Developer; Certified Secure Software Lifecycle Professional (CSSLP); C++ Certified Associate Programmer (CPA); Professional Software Developer Certification (PSD); etc.)