Welcome to Planet. We believe in using space to help life on Earth.
Planet’s mission is to *image the whole world every day and make global change visible, accessible, and actionable.* We have a people-centric approach toward culture and community and we are iterating in a way that puts our team members first and prepares our company for growth. Be a part of our mission and help build a company that is changing the world.
Our powerful cloud-based platform passes daily imagery from Planet's satellites through a fully-automated processing pipeline. The platform allows our customers to download, search, manipulate, and extract information from a dataset with global coverage. With more daily geospatial imagery data than ever in history (the platform processes upwards of 5 terabytes of imagery per day), Planet’s Engineering team is now dedicating significant effort in the development of core platform capabilities towards delivering analytics and insights from geospatial data.
Imagery Analytics is a high impact team building foundational and customer facing geospatial analytics capabilities. To achieve this we’re working on software that utilizes the latest techniques in data science, computer vision and machine learning (including deep learning). We’re looking for talented engineers that have applied product experience to join our growing team.
* Experienced Machine Learning Engineer developing predictive analytic products using multimodal geospatial data * Deploying new analytics models in production and evaluating their scalability * Collaborate closely with peers on other engineering teams and interface frequently with product and sales teams
The Must Haves:
* Strong background in Machine Learning * Strong Python software development skills * Good foundations software architecture and object oriented design * Experience with implementing machine learning models and deploying them in scalable production environments * BA/BS in Computer Science, related technical field or equivalent practical experience
The Nice to Haves: