Machine Learning Scientist

The R&D Team at Tomorrow.io is a dynamic mix of scientists and engineers. Our mission is to generate the best and most novel data and models across all times: historical, real-time, and forecast. We are seeking a Machine Learning Scientist with experience in AI weather prediction to help advance Tomorrow.io’s next-generation forecasting capabilities. In this role, you will combine atmospheric science expertise with state-of-the-art machine learning methods to improve forecast skill, including developing new ways to leverage observations from Tomorrow.io’s microwave sounder constellation. You will work collaboratively from proof-of-concept through deployment, with a focus on translating research advances into operational, customer-impacting products.

Responsibilities

Conduct research and development at the intersection of machine learning and weather prediction; Develop, train, evaluate, and improve AI-based weather prediction models, with a focus on measurable improvements in forecast skill and customer value; Develop approaches to maximize the value of observations from Tomorrow.io’s satellite constellation for weather prediction; Explore machine learning approaches for incorporating observations into forecast systems, including ML-based data assimilation and related methods; Work with large atmospheric and geophysical datasets and build reproducible, maintainable ML workflows; Collaborate with scientists and engineers to transition successful research from proof-of-concept into scalable, operational systems; Communicate results clearly through internal reviews, technical discussions, and, where appropriate, conferences and peer-reviewed publications

Requirements

Graduate degree in atmospheric science, meteorology, computer science, or a related quantitative field; 2+ years of experience developing machine learning approaches for weather prediction or closely related geoscience problems. Relevant PhD research developing ML models may count toward this experience; Hands-on experience training, evaluating, and working with deep learning models for atmospheric science; Strong understanding of ML development best practices specific to atmospheric science, including experimental design, model evaluation, testing, documentation, and code review; Strong written and verbal communication skills and the ability to explain complex technical results to both technical and non-technical audiences. Demonstrated ability to collaborate across disciplines and deliver high-quality work; Experience conducting independent research, demonstrated through publications, research leadership, open-source contributions, or other technical work

Nice to have

Knowledge of or experience with data assimilation, including traditional and/or machine-learning-based approaches; Knowledge of satellite remote sensing and experience working with satellite observations; Experience developing production-quality scientific or machine learning software; Familiarity with modern architectures used in weather and geoscience ML, including graph neural networks and transformers; AI-first mentality towards research and development (e.g., using AI-assisted development tools)

Benefits

Comprehensive health benefits; Unlimited paid time off; Flexible hours; Unlimited vacation days; Relocation assistance may be offered/available for certain roles