Computer Vision Engineer

Help us tackle the growing wildfire crisis with the latest advancements in AI and IoT Who we are The challenge: Every minute matters in wildfire response. As climate change increases the frequency and intensity of wildfires—longer fire seasons, drier fuels, and more extreme weather—new ignitions can spread rapidly, putting communities, critical infrastructure, and ecosystems at risk. Today, many wildfires are first reported by members of the public , meaning it can take valuable time to detect a fire, confirm its location and size, and mobilize responders . Fire agencies need faster, more reliable ways to detect, verify, and pinpoint new ignitions so they can respond quickly and prevent small fires from becoming catastrophic events. About Pano AI: Pano AI is the leader in AI-powered wildfire detection and intelligence, helping fire professionals detect, respond to, and contain wildfires faster and more safely. Our platform combines advanced hardware, software, artificial intelligence, satellite imagery, and other data sources to provide real-time situational awareness and actionable intelligence. Using a network of ultra-high-definition, 360-degree cameras positioned across high vantage points , Pano AI delivers a real-time view of wildfire activity, enabling faster, more informed decision-making when every second counts. We are a team of more than 175 people working in a hybrid-remote environment across North America and Australia , with headquarters in San Francisco. Our customers include government agencies, utilities, insurers, and private landowners who rely on Pano AI to help protect people, property, and natural landscapes. Pano AI currently serves customers across the United States, Australia, and Canada , monitoring more than 50 million acres worldwide. Our work has been recognized by Fast Company as one of the Top 10 Most Innovative AI Companies in 2023 and one of the World's Most Innovative Companies in 2026, ranking #1 in Sustainability. We have also been named to TIME's list of the 100 Most Influential Companies of 2025 and recognized by MIT Technology Review as one of the top climate technology companies to watch. Backed by $89 million in funding from leading investors including Giant Ventures, Liberty Mutual Ventures, Tokio Marine Future Fund, Congruent Ventures, Initialized Capital, Salesforce Ventures, and T-Mobile Ventures, we're building technology that helps communities around the world become more resilient to wildfire. Learn more at . The Role We are looking for a motivated Computer Vision Engineer to help build the next generation of cloud/edge-based vision systems for wildfire detection and environmental monitoring. In this role, you will work alongside experienced AI researchers and engineers to develop, evaluate, optimize, and deploy computer vision models on both cloud and edge devices. You will gain hands-on experience across modern computer vision, edge AI, embedded systems, and real-world AI deployment. Beyond wildfire detection, you will contribute to a variety of computer vision projects, including vegetation detection, asset recognition, instance segmentation, scene understanding, and spatial reasoning. We value curiosity, adaptability, and a willingness to learn new technologies and tackle diverse technical challenges as our products evolve. This is an excellent opportunity for an engineer who enjoys learning across the entire AI stack and wants to grow into a senior technical contributor. What you’ll do Assist in developing computer vision models for: Wildfire smoke detection Vegetation detection and classification Asset detection and recognition Instance and semantic segmentation Scene understanding and spatial reasoning Help implement and maintain machine learning and computer vision pipelines. Assist with deploying and optimizing AI models on NVIDIA Jetson and other edge platforms. Support model optimization efforts, including TensorRT conversion, quantization, and inference acceleration. Build tools for data processing, visualization, benchmarking, evaluation, and monitoring. Conduct experiments, analyze model performance, and present findings to the team. Debug inference, deployment, networking, and hardware integration issues. Contribute to continuous learning, model evaluation, and data quality improvement workflows. Collaborate closely with AI researchers, software engineers, hardware engineers, and product teams. Document experiments, engineering decisions, and best practices. Take on a variety of technical challenges as needed and continuously expand your skills across computer vision and cloud/edge AI. What you’ll bring Required BS or MS in Computer Science, Electrical Engineering, Robotics, or a related field. 1-3 years of experience (including internships or research) in software engineering, machine learning, or computer vision. Experience with Python and deep learning frameworks such as PyTorch. Understanding of machine learning fundamentals and modern computer vision techniques. Familiarity with Linux development environments. Strong problem-solving skills, curiosity, and a desire to learn. Excellent communication and teamwork skills. Preferred Experience with NVIDIA Jetson, CUDA, TensorRT, ONNX, or embedded AI platforms. Experience with OpenCV. Experience with one or more of the following: Object detection Instance or semantic segmentation Image classification Multi-object tracking Video understanding Familiarity with vision foundation models such as SAM, Grounding DINO, or DINO is a plus. Experience with cloud platforms, MLOps, or CI/CD workflows. Interest in deploying AI systems in real-world environments, particularly outdoor vision systems. 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. #J-18808-Ljbffr