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Machine Learning and Computer Vision Scientist

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Cloud to Street is the leading flood mapping platform designed to protect the world’s most climate-vulnerable communities. By harnessing global satellites, advanced science, and community intelligence, we monitor worldwide floods in near real-time and remotely analyze local flood exposure at a click of a button. Our mission is to ensure that all vulnerable governments finally access the high quality information they need to prepare for and respond to increasing catastrophes. Founded by two women at Yale and seeded by Google, Cloud to Street is or has been used by governments across 15 countries. We are on track to enable new flood protection and insurance for 10 million people in the next 5 years.


We are looking for a best-in-class Machine Learning and Computer Vision Scientist to build tools for turning satellite data into actionable insights. You should apply if you are eager to develop scalable approaches to reducing the impact of catastrophic flooding and if you are excited to build an innovative and sustainable organization. In this role, you will take ownership of large projects in Cloud to Street’s machine learning and data science efforts. You will be building the full pipeline from data collection and creation to model testing and benchmarking. You will work with a team of scientists and engineers with expertise in remote sensing (optical and radar), hydrology, climate, social vulnerability, UX, and machine learning to turn petabytes of satellite data into meaningful information to empower the world’s most vulnerable communities.

Primary Responsibilities

Characteristics of a Successful Candidate

Useful Experience

As a Cloud to Street member, you:

To Apply

Applicants are requested to send their submissions to hiring@cloudtostreet.info with: 

Applications will be accepted until the position is filled, with the goal of hiring the right candidate as soon as possible.

Cloud to Street is devoted to building an inclusive and diverse company. Black, Indigenous, and people of color; women, queer people, and all gender identities, and individuals with disabilities are especially encouraged to apply. 

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