Forests play an outsized role in climate impact. Globally, the natural cycle due to fall and regrowth alone results in larger fluctuation in GHG than all anthropogenic emissions combined. The exponential growth in wildfires, thus, is one of climate change’s most impactful implications to both nature and humanity. And it is happening right now, worldwide. In California alone, wildfires are now causing billions of dollars in damage and emitting the same amount of CO2 as 20M cars or 10M homes each year. While wildfire prevention is 20x more cost-effective than wildfire mitigation, in the current pace and scale of mitigation projects we are falling woefully behind the need.

At Earth Force, we’re developing a software solution suite that unlocks the execution of the landscape-scale wildfire prevention projects crucial for combatting the wildfire crisis.

We are seeking a highly skilled and experienced Computer Vision Engineer to join our team. As a Computer Vision Engineer, you will play a crucial role in leveraging your expertise in computer vision and machine learning techniques to extract real-time information and insights from diverse datasets. You will work closely with a cross-functional team to develop our capabilities to “see” and understand the forested environment to better guide and supervise forestry operations.

Responsibilities:

• Computer Vision Algorithm Development: Design, develop, and implement computer vision algorithms for a wide range of applications, including image classification, object detection and tracking, semantic segmentation, and vision based localization, with a focus on the forested environment.
• Multiple Sensor Integration: Integrate computer vision algorithms with data from additional sensors, enabling the analysis and interpretation of complex spatial information.
• Data Collection and Preprocessing: Direct and facilitate data collection, preprocessing and annotation efforts to allow building, training, tweaking and pruning machine learning models.
• Model Evaluation and Improvement: Evaluate the performance of computer vision algorithms and models, identify areas for improvement, and implement iterative enhancements to optimize accuracy, precision, and efficiency.
• Hardware Integration: Optimize and fine-tune algorithms and models to enable efficient implementation in a constrained hardware environment.
• Machine Learning Operations: Take machine learning models to production, while maintaining and monitoring their performance.
• Research and Innovation: Stay updated with the latest advancements in computer vision and related fields. Continuously explore and propose innovative solutions to enhance our computer vision capabilities and improve data-driven decision-making processes.
• Documentation and Reporting: Document methodologies, procedures, and findings in technical reports, research papers, or presentations. Prepare comprehensive reports and visualizations to effectively communicate analysis results.
  
  

Qualifications:

• Master's or Ph.D. degree in Computer Science, Data Science, Electrical Engineering, Robotics, or a related field.
• Proven 5-year experience as a Computer Vision / Machine Learning Engineer, building and deploying computer vision solutions.
• Expertise in computer vision techniques, including image classification, object detection and tracking, semantic segmentation and vision based localization, along with advanced techniques and tools for clustering, rapid annotation and augmentation of datasets.
• Solid understanding of statistical analysis and evaluation metrics for machine learning models.
• Proficiency in working with pre-trained visual models such as CLIP, YOLO, transfer learning and unsupervised techniques.
• Practical ML-Ops experience for deploying computer vision algorithms to embedded computing devices in real-world settings, while analyzing and monitoring their performance.
• Proficiency in popular computer vision and machine learning libraries, such as OpenCV, TensorFlow, PyTorch, or similar, along with cloud services such as AWS.
• Experience with deploying to Nvidia platforms, ROS and integration with LiDAR - an advantage.
• Knowledge and experience in geospatial data analysis, modeling, mapping software and visualization techniques - an advantage.
• Ability to work both independently and collaboratively in a fast-paced, multidisciplinary team environment.
• Excellent problem-solving skills and ability to think creatively.
• Strong communication and documentation skills.