Rainmaker Technology Corporation - Machine Learning Researcher

• Evidence of exceptional ability in machine learning research and engineering, regardless of whether it was developed in academia, industry, independent work, or another technical field. • Strong command of modern machine-learning methods and practical experience training, evaluating, and debugging models. • Strong Python skills and experience with a modern ML framework such as PyTorch, JAX, or an equivalent system. • Ability to turn ambiguous problems into measurable targets, tractable experiments, credible baselines, and working prototypes. • Sound statistical judgment, including careful treatment of leakage, distribution shift, calibration, uncertainty, and small or biased datasets. • Ability to work with noisy, sparse, multimodal, spatial, or temporal data. • Willingness to select simple methods when they are sufficient and reserve complex models for problems where they create measurable value. • Comfort working directly with scientists and engineers from domains you may not initially know. • High agency, rapid learning, and a strong bias toward useful results. • We care deeply about demonstrated technical ownership. If you have a project, system, experiment, paper, portfolio, or technical write-up that shows how you approach difficult problems, include it with your application and tell us what you personally contributed. • Experience with weather, climate, remote sensing, geospatial data, scientific ML, robotics, autonomy, aerospace, state estimation, computer vision, physical systems, or another data-constrained scientific domain. • Experience with forecasting, sequence models, probabilistic models, generative models, representation learning, sensor fusion, or data assimilation. • Experience working with radar, satellite, microwave-sounder, image, trajectory, gridded, or in-situ sensor data. • Experience taking a research model into real user workflows or production in partnership with software engineers. • Experience designing data-collection or labeling strategies when the existing dataset is insufficient. • Familiarity with atmospheric science is valuable but not required. • Starting Resources • Rainmaker will provide a dedicated compute budget, access to observations from its sensor fleet, growing proprietary datasets from operations and field campaigns, and close collaboration with atmospheric scientists and software engineers. • The data will not always arrive in a polished benchmark. Part of the role is determining what can be learned now, what ground truth must be improved, and which new observations would most increase future model performance. • What Success Looks Like • Within your first three months, you will have audited Rainmaker's most promising ML opportunities, selected a narrow and valuable initial problem, established a credible baseline, and delivered an operationally useful model or prototype with a concrete evaluation. • Within your first year, you will have established a prioritized ML roadmap grounded in actual data readiness and operational value; delivered one or more models that materially improve a scientific or operational workflow; and created reusable datasets, evaluations, or modeling foundations that accelerate subsequent work.