The Role

We are building the next generation of cloud/edge-based vision systems that combine computer vision, edge AI, PTZ cameras, and cloud intelligence to deliver real-time situational awareness for wildfire detection and beyond.

As a Senior Computer Vision Engineer, you will lead the design, development, optimization, and deployment of computer vision models and inference pipelines running on both cloud and edge devices. In addition to advancing our wildfire detection capabilities, you will develop new vision algorithms that understand complex outdoor scenes, including vegetation detection, asset recognition, object localization, and spatial reasoning (e.g., estimating distances between detected objects and critical infrastructure).

This is a hands-on technical leadership role with significant ownership of our edge AI and computer vision roadmap.

What you'll do

• 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
  • Spatial reasoning, including 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.

What you'll bring

Required

• 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.

Preferred

• 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.

Final compensation for regular full-time employees is determined by a variety of factors, including job-related qualifications, education, experience, skills, knowledge, and geographic location. In addition to base salary, regular full-time roles are eligible for equity. Benefits are tailored to local market standards and statutory requirements in the employee's country of employment, and may include health coverage, retirement or pension contributions, and paid time off. Specific benefit details will be shared during the interview process.