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 that balances open science development with sustainably building commercial applications for novel weather forecasting tools and approaches .
Requirements
5+ years of industry experience in software engineering, working on a diverse range of products, systems, and applications including operationalizing ML models and their supporting data infrastructure.
Expert proficiency in Python.
Hands-on experience designing and building cloud-native applications and infrastructure, leveraging managed services on Google Cloud Platform or Amazon Web Services.
Experience employing workflow orchestration tools (Dagster, Airflow, Prefect, etc) or other techniques for coordinating complex operational machine learning and data processing pipelines.
Demonstrated technical leadership and experience developing and deploying infrastructure to support MLOps at scale.
Ability to work independently.
Flexibility and adaptability to work on diverse projects and pivot when necessary.
Great to Have
Experience working as a technical leader in a research-focused startup and/or similar unit within a larger technical organization, emphasizing rapid R&D and subsequent operationalization of new technologies.
Experience working closely with research scientists / engineers and supporting the development of tools and processes to aid ML R&D, as a machine learning engineer or other similar role.
Familiarity with weather data, numerical weather prediction models, or machine learning weather models.
Familiarity with developing and implementing APIs for interacting with ML model inference systems and/or outputs.
Responsibilities
Collaborate with the founding team to push the boundaries of observation-driven ML weather forecasting.
Provide leadership in the architecture and implementation of core infrastructure underpinning the company’s ML weather forecast modeling pipelines for both research and operational applications.
On occasion, work with broader Product and Engineering teams to develop early proof-of-concept applications and demonstration products.
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.