Computer Vision Engineer

Zillion Technologies, Inc.; United States; On-site; USD 96,000 - 117,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401(k) plan

Job summary

Zillion Technologies, Inc. is seeking a Computer Vision Engineer to own the entire CV effort on edge devices and GPU servers. You will deploy real-time pipelines on NVIDIA Jetson hardware, handle person and intent detection, and multi-camera tracking in public and commercial spaces.

You will build training and deployment pipelines, optimize performance for edge inference, and work with RTSP streams and cross-camera re-identification to anchor detections in physical space.

Qualifications

• 5+ years of hands-on computer vision engineering experience.
• 2+ years deploying models to production edge hardware.
• Proficiency with PyTorch for training and ONNX/TensorRT for inference.
• Experience building multi-object tracking pipelines.
• Strong Python and C++ skills; comfortable with GStreamer pipelines.
• Experience with NVIDIA GPU tooling and JetPack/Jetson ecosystem.
• Experience annotating data and preparing datasets for custom detections.
• Familiarity with RTSP streams, Linux environments, and video codecs.
• Cross-camera re-identification and zone/space analytics experience.

Responsibilities

• Own end-to-end computer vision engineering on edge devices and GPU servers.
• Develop systems for person/intent detection and multi-camera tracking.
• Build training and deployment pipelines; optimize edge inference performance.
• Work with RTSP streams and cross-camera fusion to anchor detections in space.

Skills

Computer vision

Edge deployment

Python

C++

GStreamer

CUDA

cuDNN

TensorRT

PyTorch

ONNX

Tracking

Linux

Tools

YOLO detectors

JetPack/Jetson SDK

OpenCV

GStreamer

Job description

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

Compensation: 70-80/hr on w2 plus benefits, 80-85/hr on c2c. This is the targeted compensation range for this role. Please note that this range is provided as a guideline and the final offer will be based on several factors, including but not limited to, skillset and competencies, level of experience, education, certifications, and location.

Benefits: We offer a benefits package including medical, dental, vision, and 401(k).

Work Authorization: Work sponsorship is not available for this position.

Equal Employment Opportunity: We are an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, national origin, age, disability, genetic information, veteran status, sexual orientation, gender identity, or any other characteristic protected by applicable federal, state, or local law.