Rainmaker Technology Corporation

Rainmaker Technology Corporation

Machine Learning Researcher

El Segundo, CA

Sponsorship not specifiedDetected 23 hours ago
PythonMachine LearningPyTorchComputer VisionForecastingRoboticsRF EngineeringSensorsResearchCollaborationRemote Sensing

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16Unrated
Cap-exempt (no lottery)0
Sponsors this role0
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70

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About the role

  • Rainmaker is hiring its first dedicated Machine Learning Researcher.
  • You will not inherit a single predetermined model roadmap.
  • Rainmaker's long-term advantage is not a generic weather model.

Responsibilities

  • Deliver an operationally useful model or prototype within your first three months rather than spending a quarter exclusively on infrastructure or roadmap development.
  • Build models for forecasting, nowcasting, retrievals, multimodal atmospheric-state estimation, simulation, intervention analysis, and other scientific or operational applications.
  • Build datasets, labels, baselines, evaluation metrics, and validation procedures for variables that public systems do not observe or optimize well.
  • Write research-quality software and build prototypes that software engineers can help productionize when an approach proves valuable.

Requirements

  • 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.
  • Ability to work with noisy, sparse, multimodal, spatial, or temporal data.
  • Comfort working directly with scientists and engineers from domains you may not initially know.
  • 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.

Nice to have

  • 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
  • The data will not always arrive in a polished benchmark.
  • What Success Looks Like
  • Within your first year, you will have established a prioritized ML roadmap grounded in actual data readiness and operational value

Skills

  • Research at Rainmaker is attached directly to operations.

Benefits

  • Significant stock options with high potential upside as an early-stage company
  • Full health coverage (medical, dental, and vision insurance)
  • Paid parental leave for both parents
  • 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.

Company info

  • identifying the most valuable problems, determining which are ready for ML, building working models, and partnering with engineers and domain experts to turn successful research into operational systems.
  • The opportunity set includes forecasting supercooled liquid water and cloud-seeding opportunities, assimilating multimodal observations into estimates of atmospheric state, improving microwave-sounder retrievals, predicting hail, learning from intervention outcomes, and finding other high-leverage applications across research and operations.
  • It is the combination of proprietary in-cloud observations, radar and satellite data, UAS measurements, field campaigns, and repeated atmospheric interventions.
  • You will build the learning systems that turn those data into better estimates, predictions, and decisions.
  • This is initially a hands-on individual-contributor role.
  • You may eventually help recruit or technically lead an ML team if that fits your strengths and Rainmaker's needs, but management is not an initial expectation.
  • Assess potential ML projects across Rainmaker and prioritize them by operational value, data readiness, technical tractability, and time to useful results.
  • Develop methods for forecasting the occurrence, location, amount, and persistence of supercooled liquid water at scales relevant to cloud-seeding operations.
  • Combine public NWP, radar, satellite, microwave-sounder, aircraft, UAS, sounding, surface, and in-situ observations.
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This listing is sourced directly from Rainmaker Technology Corporation's careers page and normalized into a canonical job model.