Satellite Applications Specialist

About Rainmaker

Rainmaker is pioneering a modern cloud-seeding system to increase precipitation, improve water availability, and address severe-weather challenges. We combine atmospheric science, weather-resistant UAS, radar and satellite observations, numerical weather prediction, novel sensing systems, and sustainable seeding technologies to design, operate, and evaluate precipitation-enhancement programs.

Research at Rainmaker is attached directly to operations. Our scientists and engineers collect proprietary observations, deliberately intervene in atmospheric systems, evaluate the results, and use what they learn to improve the next operation.

Rainmaker is hiring a Satellite Remote Sensing Researcher to expand how we use satellite observations across research and operations. You will join experienced satellite scientists and add both scientific capacity and stronger computational execution to the team.

Your work may include developing and validating retrievals, building automated data products, supporting operational cloud analysis, fusing satellite observations with radar and aircraft data, and pursuing applied research under the guidance of Rainmaker's existing researchers. Our highest-priority observations include microwave sounders and other polar-orbiting instruments, while geostationary imagery provides the coverage and temporal continuity needed for operations.

We are open to an earlier-career scientist with excellent fundamentals, high agency, and evidence of strong computational work. You should be excited to learn from experienced researchers while independently owning bounded projects and turning scientific methods into durable workflows.

• Develop, reproduce, improve, and validate satellite retrieval algorithms relevant to clouds, precipitation, atmospheric state, and cloud‑seeding opportunities.
• Work extensively with microwave sounders and other polar-orbiting observations while incorporating geostationary imagery for coverage, temporal context, and operational monitoring.
• Build automated, documented workflows that transform raw or low-level observations into useful scientific and operational products.
• Evaluate existing SLW, cloud‑phase, cloud‑top, moisture, temperature, precipitation, and related products against independent observations.
• Collocate satellite data with radar, NWP, soundings, surface observations, and Rainmaker aircraft, UAS, or in‑situ measurements.
• Quantify retrieval bias, uncertainty, spatial representativeness, latency, coverage, and failure modes by meteorological regime.
• Support real‑time and retrospective cloud analysis for cloud‑seeding operations.
• Improve the reliability, speed, scalability, and usability of satellite‑data processing across the science team.
• Contribute to research directions selected with Rainmaker's existing satellite scientists.
• Work with ML engineers when data‑driven retrievals or multimodal models are justified by the data.
• Work with software engineers to transition successful research workflows into reliable operational systems.
• Communicate scientific results, limitations, and uncertainty clearly to researchers, operators, and engineers.

• A degree in atmospheric science, meteorology, remote sensing, physics, applied mathematics, electrical engineering, computer science, or a related field, or equivalent evidence of exceptional remote‑sensing ability.
• Strong scientific understanding of satellite observations, radiative transfer, retrievals, calibration, validation, or measurement uncertainty.
• Strong Python and quantitative‑analysis skills.
• Experience working computationally with satellite data, preferably including microwave or polar‑orbiting observations.
• Ability to implement, evaluate, and document scientific methods personally rather than immediately handing them off to a software team.
• Experience with multidimensional scientific data and tools such as Num Py, Sci Py, pandas, xarray, Dask, or equivalent systems.
• Ability to build reproducible data‑processing and validation workflows.
• Comfort receiving research guidance while independently owning a clearly defined project.
• High agency, learning velocity,…