Machine Learning Engineer, Computer Vision & Signal Processing

Job Description

Digital Global Systems is seeking a Machine Learning Engineer specializing in computer vision and signal processing to support the development, deployment, and optimization of image-based detection and RF signal classification models within the CLEARSITE™ platform.

This role focuses on building real-time ML/CV systems using waterfall and spectrogram imagery combined with RF waveform data. The engineer will work closely with the ML/CV Lead to deliver high-performance models deployed on GPU and edge hardware, including NVIDIA A-series GPUs and NVIDIA Jetson platforms.

Key Responsibilities

• Design, train, and optimize computer vision models for RF signal detection using waterfall and spectrogram imagery
• Develop and maintain energy detection and object detection models with strict performance and latency targets
• Train and optimize single-stage object detection models (e.g., YOLO-family architectures) for real-time inference
• Implement instance segmentation models to improve confidence and classification accuracy in ambiguous detection scenarios
• Build and maintain signal classification pipelines using waveform feature extraction and composite fingerprinting
• Optimize models for GPU and edge deployment, including TensorRT conversion and inference acceleration
• Curate, label, and manage training datasets from field deployments; implement augmentation pipelines
• Develop evaluation frameworks including precision/recall, ROC analysis, calibration, and per-band metrics
• Ensure model outputs conform to standardized detection schemas with calibrated confidence scores
• Support field validation, testing campaigns, and iterative model retraining
• Collaborate cross-functionally with systems, hardware, and field engineering teams

Required Qualifications

• 3+ years of experience in applied machine learning and/or computer vision
• Hands-on experience deploying ML models into production environments
• Strong knowledge of object detection architectures (YOLO-family or similar)
• Proficiency in PyTorch and/or TensorFlow
• Experience with model evaluation, performance metrics, and confidence calibration
• Understanding of GPU inference optimization (batching, quantization, pruning)
• Strong Python skills and experience with NumPy, OpenCV, and ML tooling
• Experience building and maintaining image preprocessing pipelines

Preferred Qualifications

• Experience optimizing models with TensorRT for NVIDIA GPUs
• Familiarity with NVIDIA Jetson platforms (JetPack SDK, cuDNN, Jetson AGX Orin)
• Experience with instance segmentation models (QueryInst, Mask R-CNN, or similar)
• Background in RF signal processing, spectrograms, or waterfall imagery
• Experience with waveform or signal classification
• Familiarity with multi-RAT environments (LTE, 5G NR, Wi-Fi, BLE)
• Experience deploying ML models to embedded or edge environments with strict latency constraints