What You'll Do
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.
What We're Looking For
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.
Preferred Qualifications
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.
What Success Looks Like
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.
Benefits
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
