Rainmaker Team

• 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 NumPy, SciPy, 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, and willingness to engage with operational users of the resulting products.

• Graduate research or industry experience in satellite meteorology, microwave remote sensing, cloud or precipitation retrievals, atmospheric sounding, or Earth observation.
• Experience working with both polar-orbiting and geostationary observations.
• Familiarity with radiative-transfer models, retrieval inversion, Bayesian estimation, uncertainty quantification, or statistical and ML retrieval methods.
• Experience collocating satellite data with radar, aircraft, in-situ sensors, soundings, or NWP.
• Familiarity with cloud microphysics, mixed-phase clouds, supercooled liquid water, precipitation processes, or weather modification.
• Experience with cloud-scale or large-volume geospatial processing in local, HPC, or cloud environments.
• Experience creating data products used in operational forecasting or decision-making.

Within your first three months, you will have taken ownership of one bounded retrieval or satellite-data workflow selected with the existing team. You will have reproduced and validated the current baseline, then delivered a meaningful improvement in retrieval quality, automation, coverage, latency, scalability, or operational usability.

The result will be a documented and repeatable workflow that Rainmaker's broader science or operations team can use. Success does not require inventing a novel retrieval in one quarter; it requires producing trustworthy computational leverage and demonstrating clear scientific judgment.

Within your first year, you will have expanded the team's capacity across microwave, polar-orbiting, and geostationary observations while making multiple satellite workflows more validated, automated, and operationally useful.

• Significant stock options with high potential upside as an early-stage company
• 401(k) with employer matching
• Full health coverage (medical, dental, and vision insurance)
• Relocation assistance provided (if applicable)
• Unlimited PTO
• Paid parental leave for both parents
• Lunch provided when working in-office and a fully stocked kitchenette
• Free EV charging at the HQ