McLean, VA

Machine Learning Engineer, Computer Vision & Signal Processing

Location: In-Person, Hybrid, or Remote

Salary Range: Contact for Details

Employment Type: Full-Time

Location: In-Person, Hybrid, or Remote

Salary Range: Contact for Details

Location: In-Person, Hybrid, or Remote

Salary Range: Contact for Details

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

Resume Submission

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