Computer Vision Applied Scientist

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.

Pano AI is on TIME's list of the 100 Most Influential Companies of 2025! MIT Technology Review listed Pano as one of the top 15 climate tech companies to watch in 2024, and Fast Company named Pano AI one of the Top 10 most innovative companies in AI of 2023. We’ve also been featured in the Wall Street Journal, Bloomberg, and CNBC News. Pano AI’s dozens of government and enterprise customers span 16 states in the U.S., five states in Australia, and BC, Canada, and we are currently monitoring over 30 million acres of land. Pano AI has raised $89M in venture capital funding from Giant Ventures, Liberty Mutual Ventures, Tokio Marine Future Fund, Congruent Ventures, Initialized Capital, Salesforce Ventures, and T-Mobile Ventures. Learn more at https://www.pano.ai/.

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 model, 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.

• 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 fires, pinpoint fires in 3D, and predict how they'll spread.
• Constantly making our models and MLOps better, combining different sensor data, and using our existing domain knowledge to improve performance and real-time processing.
• Working closely with the AI and platform teams to deliver awesome solutions for Pano's customers.
• Showing off Pano AI's tech with solid real-world performance numbers.

• A PhD or MS in Computer Science, Electrical Engineering, Robotics, or related field, with a focus on computer vision, 3D perception, or geospatial AI.
• At least 2 years of industry experience in computer vision, foundational vision model, and multi-modal LLM.
• Strong Python coding skills, especially with PyTorch.

• Experience working with geospatial data, GIS, or remote sensing.
• Experience working with cloud systems.
• Excellent communication skills.
• Publications in top-tier vision or ML conferences.

Pano is an equal opportunity employer committed to recruiting and supporting our team-members regardless of where they come from. We do not discriminate on the basis of race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.