AI​/ML Engineer, Senior - WFH

Position: AI/ML Engineer, Senior - WFH1659
Clearance Level: Public Trust (Secret Eligible)

ship:Required

Job Classification:1099/Consultant

Location:Remote

Years of Experience:5 7 years of relevant experience

Education Level:BS or MS in Electrical Engineering, Computer Science, Applied Mathematics, or a closely related quantitative field. Experience may be considered in place of education requirement.


Briefly Describe the Work:

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

Career level with a complete understanding and wide application of machine learning principles and data science techniques. Working under general direction from the Principal AI/ML Engineer, executes independently on assigned modeling and analysis tasks, contributes to pipeline development, and produces reproducible, well-documented results.

Bachelor s or Master s (or equivalent) with 5 7 years of hands-on applied experience.


Required Skills:

• 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 Tensor Flow for model development, Pandas/Num Py 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
• Ha…