Machine Learning Engineer

Summary

Join Floodbase, a climate change solutions company, as a Machine Learning Engineer. You will build algorithms to synthesize data from various sources into actionable insights for flood risk assessment and parametric insurance products. This role involves ownership of large projects, from data selection to model deployment and analysis. You'll collaborate with a team of scientists and engineers, contributing to cutting-edge AI climate science. The position offers remote work options with core hours in EST or a hybrid model in Brooklyn, NY. You will be a technical leader, mentoring team members, and translating research into robust products. Opportunities for publication and conference presentations are available.

Requirements

• Be an intellectually curious and driven individual who is interested in working on innovative, impactful problems to address the climate crisis
• Have a Bachelor’s in a quantitative discipline with 4+ years of industry experience or Master’s or PhD with 1-2+ years of industry experience
• Have demonstrated experience working in interdisciplinary teams solving complex problems that rarely have a clean, correct solution
• Have high coding proficiency in Python: can independently develop code and make and review contributions to a shared repository
• Be familiar with open-source geospatial ecosystems (rasterio/gdal, shapely, geopandas, xarray/rioxarray, dask)
• Have mastery of the fundamentals of scientific data analysis, statistics, and machine learning
• Have a proven track record (open source projects, Kaggle, publications, etc) of developing new models for innovative applications
• Keep up with state-of-the-art modeling techniques, like foundation models, and identify practical approaches for scalable solutions to environmental problems

Responsibilities

• Use your analytical expertise to derive robust insights from large structured and unstructured datasets of observations, hydrologic model outputs, and auxiliary records (stream gauges, water occurrence, …) that can be incorporated into the Floodbase parametric insurance product
• Automate the manual validation process of our historical flood data for individual areas of interest (AOIs) by finding robust rules leveraging multiple validation data sources
• Estimate risk of flooding and its associated uncertainty bounds informed by satellite observations for parametric insurance product design in collaboration with insurance market experts and product managers
• Find ways to add uncertainty estimates to our flood parametric product by sampling additional data points spatially around the AOIs or with statistical resampling methods like bootstrapping
• Utilize your proficiency in Machine Learning to advance the state of the art in flood detection from large datasets of observational data and models that can specifically inform the flood response needs of Floodbase customers
• Train a global flood segmentation model on Sentinel-2 data that improves on challenging urban scenes and inside mountain and cloud shadows
• Be a technical leader in the team and advocate for customer-centric product development based on time-bound scientific hypotheses-oriented experiments
• Manage, mentor, and recruit exceptional data scientists from a variety of backgrounds
• Visualize and explain work through presentations and notebooks
• Work closely with our team of machine learning engineers, research scientists, and software developers to translate rigorous science and research into robust products

Preferred Qualifications

Have prior experience with satellite imagery and/or geospatial modeling

Benefits

The role is remote, with core hours in EST or hybrid based in Brooklyn, NY