Machine Learning Engineer


Cloud to Street is the world’s leading remote flood mapping platform. We use global satellites and remote sensing AI to monitor flood risk and detect worldwide floods in real-time. Seeded by Google, we have been used by governments across almost 20 countries for disaster relief efforts. Partnering with top insurers, we are now launching the first commercial parametric flood insurance product to better protect climate-vulnerable communities.


We are looking for an experienced Machine Learning Engineer to help us scale up our Deep Learning efforts to turn optical and radar satellite imagery into actionable insights. In this role, you will take ownership of large projects in Cloud to Street’s Deep Learning agenda - building the full pipeline from training data collection to model training and testing. You will work with a team of scientists and engineers with expertise in Machine Learning, remote sensing, radar and hydrology to turn petabytes of satellite data into meaningful information to empower the world’s most vulnerable communities.

This role is based in Brooklyn, NY and is remote until further notice. Remote work is possible within UTC -5 to UTC +1 time zones (Eastern Standard Time to Central European Time).

Who You Are


To Apply

Send your resume to with Machine Learning Engineering in the subject line. Let us know why you would like to join Cloud to Street and how you can best contribute. Applications will be open 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.


Subscribe to our newsletter

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

Connect with us

Any questions? Get in touch.
For press inquiries, email:
©2021 Cloud to Street, PBC.
All rights reserved.