Senior Computer Vision Engineer
$195,000–$255,000 year
RemoteUnited States
Job Summary
Design and implement cloud/edge AI architectures for real-time computer vision applications focused on wildfire smoke detection, vegetation classification, and asset recognition. Develop lightweight detection, segmentation, and temporal reasoning models optimized for ARM64, CUDA, and NVIDIA Jetson platforms. Build and optimize inference pipelines for RGB, NIR, and multi-camera systems while leading model compression efforts including quantization and pruning. Improve inference latency, throughput, and power efficiency across hybrid edge-cloud workflows. Mentor junior engineers and establish best practices for edge AI development. Collaborate with researchers, software, hardware, and product teams to deploy, monitor, and update systems.
Required Qualifications
- MS or PhD in Computer Science, Electrical Engineering, Robotics, or a related field
- 5+ years of industry experience in computer vision or machine learning
- Strong experience with PyTorch and modern deep learning architectures
- Experience deploying AI models to edge devices such as NVIDIA Jetson, embedded GPUs, or similar platforms
- Strong understanding of CUDA, TensorRT, ONNX, model optimization, and inference acceleration
- Object detection
- Semantic or instance segmentation
- Image classification
- Video understanding
- Multi-object tracking
- Depth estimation or 3D computer vision
- Strong Python and C++ programming skills
Desired Qualifications
- Experience with outdoor vision systems, autonomous systems, robotics, surveillance, remote sensing, or geospatial AI
- Experience with PTZ camera systems
- Experience with multi-camera calibration, localization, and distributed camera systems
- Experience with spatial AI, scene understanding, or geometric computer vision
- Experience estimating object distances or reasoning about spatial relationships using monocular, stereo, or multi-view imagery
- Experience with MLOps and continuous learning pipelines
- Familiarity with foundation vision models (e.g., DINOv2/DINOv3, SAM, Grounding DINO, Florence, or similar)
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