Senior Computer Vision Engineer
About The Position
Pano AI is a leader in AI-powered wildfire detection and intelligence, utilizing a network of ultra-high-definition cameras and advanced AI to provide real-time situational awareness. The company is building the next generation of cloud/edge-based vision systems that combine computer vision, edge AI, PTZ cameras, and cloud intelligence for wildfire detection and beyond. This role involves leading the design, development, optimization, and deployment of computer vision models and inference pipelines for both cloud and edge devices. The engineer will advance wildfire detection capabilities and develop new vision algorithms for complex outdoor scenes, including vegetation detection, asset recognition, object localization, and spatial reasoning. This is a hands-on technical leadership role with significant ownership of the edge AI and computer vision roadmap.
Requirements
- 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.
- Experience with one or more of the following: 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.
Nice To Haves
- 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) is a plus.
Responsibilities
- Design and implement cloud/edge AI architectures for real-time computer vision applications.
- Develop computer vision models for wildfire smoke detection, vegetation detection and classification, asset detection (e.g., power lines, utility poles, buildings, roads), scene understanding and semantic segmentation, and spatial reasoning (e.g., estimating distances and relationships between detected objects and nearby assets).
- Build lightweight detection, segmentation, classification, and temporal reasoning models for real-time inference.
- Port and optimize deep learning models for ARM64, CUDA, TensorRT, ONNX, and NVIDIA Jetson platforms.
- Build and optimize both cloud and edge inference pipelines for RGB, NIR, PTZ, and multi-camera systems.
- Develop hybrid edge-cloud AI workflows that balance latency, bandwidth, and compute efficiency.
- Improve inference latency, throughput, memory usage, and power efficiency.
- Lead model compression efforts, including quantization, pruning, and knowledge distillation.
- Design deployment, monitoring, OTA update, and observability capabilities for edge AI systems.
- Collaborate closely with AI researchers, software engineers, hardware engineers, data engineers, and product teams.
- Mentor junior engineers and establish best practices for edge AI and computer vision development.
Benefits
- Equity
- Health coverage
- Retirement or pension contributions
- Paid time off
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What This Job Offers
Job Type
Full-time
Career Level
Senior
