Senior Computer Vision Engineer/Scientist (Satellite Image Analysis)

  • Pano AI
  • Remote
  • Apr 26, 2022
Full time role Engineering: Software

Job Description

Help us tackle the growing wildfire crisis with the latest advancements in AI and IoT

Who we are

The problem: Every minute matters in fire response.  As climate change amplifies the intensity of wildfires—with longer fire seasons, dryer 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:  Founded in 2020, Pano is building the world’s first fully-integrated disaster management platform focused on climate change resilience. Pano’s SaaS solution leverages advanced hardware, software, and artificial intelligence technologies to modernize the disaster preparedness industry. Pano Rapid Detect, the company’s first product, is already a leader in wildfire early detection and intelligence, with millions of dollars in revenue  stretching across 4 states in the U.S. and 2 states in Australia. Our customers include PG&E, Portland General Electric, the Big Sky Montana Fire Department, and Southern Cross Forests.

Our team is composed of seasoned technology professionals from companies such as Cisco, Apple, and Nest. Headquartered in San Francisco with an office and factory in the Mission District, our hybrid team works from locations around the world. Founded in mid-2020, we’ve raised over $8M from leading VC funds and prominent angel investors, including the CEOs of Gitlab, HomeLight, and People.AI. 

Role overview

Pano is looking for an experienced Computer Vision (CV)  expert to develop and execute Pano’s roadmap for our smoke detection CV model(s) for DayTime Panoramic Camera, NightTime IR Camera and Satellite Images.  This individual is responsible for developing CV models for early detection of Fire/Smoke in different types of cameras (Panoramic, IR and Satellite), maintaining database of fire/smoke labels, working with labellers and data engineering for implementing Active Learning pipelines, preprocessing and postprocessing to reduce the FP and FN and adopt the models for different geographic locations/landscapes and weather conditions. 

Ideal candidates have deep expertise in computer vision in Satellite, Infra-Red (IR)/Near Infra-Red (NIR) Camera, Thermal Camera and other sensors. This position is remote but company meetings are scheduled for the Pacific time zone. This individual will report to Pano’s Engineering Manager of ML.

Key responsibilities

  • Design and develop CV models for smoke detection in Satellite, IR/NIR camera images. Over time, these models will need to accommodate a growing number of camera feeds and camera types, as well as an ever-widening range of environmental conditions, while at the same time demonstrating continuous improvement in speed and accuracy.
  • Alignment of Geospatial regions of Satellite images with Panoramic/IR/NIR images.
  • Actively scout the algorithms landscape for optimization of feature extractions and respective predictions.
  • Responsible for smoke detection models for production environment, including conducting experiments on new candidate models and assessing the performance of models in production as well as against a test set.
  • Optimize the implementation of production near-real-time inference, working in partnership with a Python/full stack developer.
  • Oversee the training data cycle and management of training data, with support from multiple ML ops resources.
  • Contribute to intellectual property claims, research papers, and grant applications.
  • Track the state of the art in the field and investigate novel models, labeling techniques, and ML Ops tools that expand the scope and capabilities of our products.
  • Communicate the capabilities of our technology internally and externally in an engaging and educational way.
  • Support Pano’s commitments to data privacy and ethical AI.

Desired qualifications & experience

  • 2+ years recent experience in developing and implementing production deep learning models for computer vision tasks specifically object detection in satellite, IR and NIR images, including heavy involvement with or ownership of the infrastructure supporting model development (data, large scale training, eval, etc)
  • 3+ years of ML experience overall, with excellent ML fundamentals demonstrated by models successfully deployed in production
  • Experience working with traditional CV approaches, such as filters and optical flow
  • Demonstrated experience and skills in programming image analytics, statistical data analysis, clustering/classification, multivariate analysis and machine learning complemented by strong data fusion and data visualization
  • Good SWE fundamentals and ability to demonstrate SWE experience in both high and low-level languages (Python and C++)
  • Bonus: Experience in deploying models in real time environments. Optimizing architectures to explore the accuracy vs. compute tradeoff 
  • Bonus: Sound understating of Sensor Analytics and GIS environments
  • Bonus: Experience working in a lean startup environment
  • Bonus: Masters or PhD in Computer Vision, Machine Learning, or other relevant field
  • Bonus: Experience with geospatial applications, data formats, and concepts such as GeoTIFFs, NTFF, coordinate reference systems, GIS, GDAL

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.

Application Instructions

Interested candidates should forward a resume or a link to your LinkedIn profile to

Organization Type


Organization Size



Climate Adaptation