Responsibilities
- Create and refine machine learning models for identifying RF emitters, including extracting features from sensor inputs, building training workflows, assessing model performance, and improving models iteratively based on outcomes
- Perform detailed data analysis on RF sensor data using Python and Jupyter, writing analytical scripts, analyzing feature patterns, detecting data quality problems, and documenting insights
- Develop and sustain machine learning data pipelines that ingest NDF sensor data, apply aggregation and preprocessing rules, generate training sets, and operate reliably on edge devices without cloud or GPU support
- Work with senior technical staff to examine RF data quality, feature reliability, and model behavior under interference, using code to trace error sources, verify hypotheses, and replicate results
- Document experiments, model settings, and outcomes clearly, ensuring reproducibility through strict version control and contributing to monthly updates and team knowledge exchange
Work Arrangement
Remote (Worldwide)
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
