AI​/ML Engineer, Senior - WFH Security Clearance

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

* 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 (Flat Buffers, Protocol Buffers) and high-throughput…