Be one of the initial hires at a remote startup, started by experienced entrepreneurs, developing a transformative approach to earth system modeling.
Build the world’s best weather forecast using an approach that learns directly from observational data.
Join a multi-disciplinary team committed to open science and sharing results with the broader weather and climate communities.
Requirements
BS, or MS in computer science, mathematics, applied statistics, machine learning, physics, meteorology, geography, or equivalent industry experience.
3+ years of industry experience developing data ingestion and processing infrastructure, ideally working with a multitude of geospatial data from weather/climate models, satellites, radar, and types of observation systems.
Expert proficiency in Python.
Hands-on experience designing and building applications on Google Cloud Platform leveraging managed services/products.
Practical experience working with diverse weather/environmental data formats, including HDF5, NetCDF, Tiff/GeoTiff, BUFR, GRIB, and various weather radar formats.
Experience and proficiency working with systems and tools designed for large-scale data processing and archival, including Parquet, Zarr, Apache Beam/Google Cloud Dataflow, BigQuery, etc.
Experience designing, rapidly prototyping, and evaluating complex data processing systems
Proficiency in communicating system designs to and with input from technical stakeholders including scientists and engineers.
Ability to work independently.
Flexibility and adaptability to work on diverse projects and pivot when necessary.
Great to Have
Experience leveraging workflow orchestration tools to automate data processing pipelines, such as Dagster, Airflow, or Prefect.
Knowledge about weather and climate observation systems and data, especially from satellite platforms.
Familiarity with NOAA/NASA/ESA/JAXA satellite data and other datasets commonly leveraged for numerical weather prediction and data assimilation applications.
Familiarity with common open-source tools developed in the world of weather/climate for interacting with legacy data, including eccodes package from ECMWF and the various NCEPLIBS-* from NOAA.
Responsibilities
Collaborate with the founding team to push the boundaries of observation-driven ML weather forecasting.
Design, implement, and operationalize terascale weather data ingestion and processing systems, optimizing for low latency and frictionless integration into ML workflows.
Work closely with core research and engineering staff to coordinate and prioritize dataset acquisition and preparation.
Establish best practices and workflows for data engineering across the company’s development portfolio.
Promote engineering best practices by conducting code reviews and ensuring high-quality code.