Computer Vision Engineer

Zillion Technologies, Inc.; Virginia (MN); Hybrid; USD 150,000 - 210,000; Full time

Job summary

Zillion Technologies, Inc. is seeking a Computer Vision Engineer to own end-to-end CV engineering for edge deployments on NVIDIA Jetson hardware and GPU servers.

You will build, train, optimize, and deploy models that operate in public and commercial spaces, addressing real-world conditions with robust pipelines. You will implement person and intent detection, multi-camera tracking, and zone-based analytics, plus training/inference pipelines and edge deployment optimization.

Qualifications

• 5+ years of hands-on computer vision engineering experience, including edge deployment on NVIDIA Jetson hardware.
• Deep expertise with YOLO detectors: training, fine-tuning, and real-world failure handling.
• Proficiency with PyTorch for training and ONNX/TensorRT for inference optimization; quantization experience.
• Experience building multi-object tracking pipelines (e.g., SORT/DeepSORT/BoT-SORT) and evaluation tradeoffs.
• Strong Python and C++ skills for pipeline development and GStreamer graph work.
• Familiarity with NVIDIA CUDA, cuDNN, TensorRT, and JetPack/Jetson SDK.
• Experience creating annotation pipelines and managing datasets for custom object detection.
• Comfort with RTSP camera streams on Linux and H.264/H.265 decoding.
• Experience with cross-camera re-identification and homography-based fusion.
• Knowledge of zone-based spatial analytics and camera-to-world calibration.

Responsibilities

• Own the computer vision engineering effort from data to deployment in edge environments.
• Build edge CV pipelines running on Jetson devices and GPU servers.
• Train, optimize, and deploy models handling real-world conditions in public/commercial spaces.
• Develop multi-camera tracking and zone-based analytics features.
• Create training and deployment pipelines, including edge inference and performance tuning.

Skills

Computer Vision

Edge deployment

YOLO detectors

PyTorch

ONNX/TensorRT

Multi-object Tracking

C++

Python

GStreamer

CUDA/cuDNN

Jetson toolkit

Dataset management

Tools

GStreamer

Job description

Type of Employment : Full time with Zillion Technologies

Location: Ashburn, VA or Bethesda MD (Hybrid)

The Computer Vision Engineer role owns all computer vision engineering effort. You will work on edge-deployed CV pipelines running on NVIDIA Jetson hardware, and GPU servers — building, training, optimizing, and deploying models that handle real-world conditions in public and commercial spaces.

You will build systems with person and intent detection, multi-camera tracking, track package placement and removal events at shelf zones. You will also build model training and deployment pipelines and perform edge deployment and performance optimization.

Required

• 5+ years of hands-on computer vision engineering experience, with at least 2 years deploying models to production edge hardware (not just cloud or research environments)
• Deep practical experience with the YOLO family of detectors — training, fine-tuning, hyperparameter tuning, and understanding failure modes in real-world conditions
• Proficiency with PyTorch for model training and ONNX / TensorRT for inference optimization; hands-on experience with INT8 or FP16 post-training quantization
• Experience building multi-object tracking pipelines — SORT, DeepSORT, BoT-SORT, or equivalent — and understanding the tradeoffs between tracker accuracy, computational cost, and track stability
• Solid Python and C++ skills for pipeline development; comfort reading and modifying GStreamer pipeline graphs
• Experience with NVIDIA GPU tooling: CUDA, cuDNN, TensorRT, and the JetPack / Jetson SDK ecosystem
• Experience building annotation pipelines and managing training datasets for custom object detection tasks — not just using pre-trained models on standard benchmarks
• Comfort working with RTSP IP camera streams in Linux environments; understanding of H.264/H.265 codec pipeline and hardware decode
• Experience with cross-camera person re-identification — OSNet, FastReID, or equivalent architectures; homography-based multi-camera fusion
• Experience with zone-based spatial analytics — polygon intersection, floor-plane projection, homography calibration from camera to world coordinates

Strongly preferred

• Experience building CV systems for retail, logistics, or security environments where the camera network covers a physical space and detections must be spatially anchored
• Familiarity with Roboflow or CVAT for dataset management and annotation workflow automation
• Experience with the NVIDIA Metropolis or DeepStream framework — even if ultimately not used, understanding where these add value vs a custom open-source stack
• Prior work on privacy-preserving CV pipelines — on-device inference, derived-data-only architectures, anonymization techniques