Senior AI/ML Engineer — Remote Edge RF Signal Processing

AIToolboard; Reston (VA); Remote; USD 140,000 - 190,000; Full time

Job summary

Global InfoTek, Inc. is seeking a Senior AI/ML Engineer to advance an R&D program on passive RF emitter identification and edge data processing.

You will design, train, and evaluate ML models, perform exploratory analysis on sensor datasets, and implement robust, CPU-only inference pipelines for air-gapped environments. The role emphasizes collaboration with researchers and software engineers, deep learning proficiency, and documenting reproducible experiments while operating without cloud

Qualifications

• 5+ years hands-on ML/data science or RF signal processing.
• Proficiency in Python for ML with PyTorch or TensorFlow.
• Experience with metric learning and Siamese networks on real-world data.
• Ability to develop ML pipelines on Linux with CPU-only inference.
• Experience handling data quality issues in imperfect sensor data.

Responsibilities

• Design, build, and validate ML models for RF emitter identification with feature engineering from sensor data.
• Conduct exploratory data analysis on RF sensor datasets using Python/Jupyter.
• Implement and maintain ML data pipelines ingesting NDF sensor streams for edge hardware.
• Collaborate with ML leads to investigate data quality, attribution reliability, and feature behavior.
• Document experiments, configurations, and results for reproducibility and reporting.

Skills

Python for ML

PyTorch/TensorFlow

Pandas/NumPy

Scikit-learn

Deep learning

CPU-only inference

Data quality handling

Edge ML pipelines

Education

B.S./M.S. in EE/CS/Applied Math

Tools

FlatBuffers

Protocol Buffers

Job description

Global InfoTek, Inc. is seeking a Senior AI/ML Engineer to advance an R&D program on passive RF emitter identification and edge data processing.

You will design, train, and evaluate ML models, perform exploratory analysis on sensor datasets, and implement robust, CPU-only inference pipelines for air-gapped environments. The role emphasizes collaboration with researchers and software engineers, deep learning proficiency, and documenting reproducible experiments while operating without cloud