Job Description
### Who We Are
The problem: Every minute matters in fire response. As climate change amplifies the intensity of wildfires—with longer fire seasons, drier fuels, and faster winds—new ignitions spread faster and put more communities at risk. Today, most wildfires are detected by bystanders and reported via 911, meaning it can take hours to detect a fire, verify its exact location and size, and dispatch first responders. Fire authorities need a faster way to detect, confirm, and pinpoint fires so that they can quickly respond—preventing small flare-ups from becoming devastating infernos.
### About Pano
We are a 130+ person growth-stage hybrid-remote start-up, headquartered in San Francisco. We are the leader in early wildfire detection and intelligence, helping fire professionals respond to fires faster and more safely—with the right equipment, timely information, and enhanced coordination—so that they can stop a new ignition before it grows. Pano AI combines advanced hardware, software, and artificial intelligence into an easy-to-use, web-based platform. Leveraging a network of ultra-high-definition, 360-degree cameras atop high vantage points, as well as satellite and other data feeds, Pano AI produces a real-time picture of threats in a geographic region and delivers immediate, actionable intelligence.
### The Role
The Computer Vision Applied Scientist will be a part of the AI team that builds and deploys deep learning models to find, classify, locate, and track wildfires from cameras and satellites. You will be working on computer vision, foundational vision models, multi-modal LLM, sensor fusion, 3D localization, and understanding scenes. You'll work with platform engineers to create new software to help with this huge environmental challenge. As a self-motivated and enthusiastic member of our team, you will work in an agile environment and balance developing critical new features with improving the core technical underpinnings of our system.
### What You’ll Do
- Developing smart geospatial algorithms for real-time awareness and predicting environmental risks from cameras and satellites.
- Owning the deep learning models that understand environments, tell the difference between wildfires and other objects, and improve detection accuracy.