Windborne Systems

1 week ago

Machine Learning Research Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 4 hours agoStatus: Live

Job Summary

Build AI-driven weather models that incorporate real-time balloon, satellite, and station observations to improve global forecasts. Work on data assimilation, progress toward a weather foundation model, and tackle messy, large geospatial datasets. Run rapid experiments, translate wins into durable infrastructure, and help shape research direction through hypotheses and evaluation—bridging ML research and engineering in a mission-driven team.

Location: Redwood City

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Develop AI-based data assimilation methods to incorporate real-time balloon, satellite, and station observations into forecasts.
•Advance a foundation model for weather that can predict many weather datasets and related variables and data products.
•Source, clean, and combine large weather datasets with inconsistent formats and quality, and build systems that make useful data easier to reuse.
•Run rapid experiments: test ideas quickly, learn from failures, and pursue incremental improvements.
•Turn successful research into reusable infrastructure and help set research direction through hypotheses and ongoing ML literature tracking.

Pay and Benefits

Salary: USD 140,000 - 240,000 annually

Perks:401kDentalHealth InsuranceVisionPaid LeaveEquityMeal Allowance

Key Requirements

•Strong research taste: identify important questions, design experiments, and recognize when results are real.
•Deep enthusiasm for machine learning with a desire to understand models in detail rather than treating them as black boxes.
•Strong Python and PyTorch skills, including developing and debugging nontrivial ML systems.
•Experience working with large, messy datasets and building dependable pipelines or abstractions around them.
•Comfortable iterating quickly and bridging between research and engineering as tools or domains change.

Experience:WeatherClimateGeospatial dataScientific machine learningComputer visionTime-series forecasting

Skills:Research tasteProblem identificationExperiment designIterating quicklyCross-functional collaboration

Tech Stack:PythonPyTorch

Company Brief

Windborne Systems

Develops airborne systems and technologies for wind measurement, environmental monitoring, and renewable energy applications, focusing on hardware design, sensor integration, and data solutions to support wind resource assessment and operational monitoring.

Industry: Climate Tech

Website

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Windborne Systems

1 week ago

Machine Learning Research Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 4 hours agoStatus: Live

Job Summary

Location: Redwood City

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Develop AI-based data assimilation methods to incorporate real-time balloon, satellite, and station observations into forecasts.
•Advance a foundation model for weather that can predict many weather datasets and related variables and data products.
•Source, clean, and combine large weather datasets with inconsistent formats and quality, and build systems that make useful data easier to reuse.
•Run rapid experiments: test ideas quickly, learn from failures, and pursue incremental improvements.
•Turn successful research into reusable infrastructure and help set research direction through hypotheses and ongoing ML literature tracking.

Pay and Benefits

Salary: USD 140,000 - 240,000 annually

Perks:401kDentalHealth InsuranceVisionPaid LeaveEquityMeal Allowance

Key Requirements

•Strong research taste: identify important questions, design experiments, and recognize when results are real.
•Deep enthusiasm for machine learning with a desire to understand models in detail rather than treating them as black boxes.
•Strong Python and PyTorch skills, including developing and debugging nontrivial ML systems.
•Experience working with large, messy datasets and building dependable pipelines or abstractions around them.
•Comfortable iterating quickly and bridging between research and engineering as tools or domains change.

Experience:WeatherClimateGeospatial dataScientific machine learningComputer visionTime-series forecasting

Skills:Research tasteProblem identificationExperiment designIterating quicklyCross-functional collaboration

Tech Stack:PythonPyTorch

Company Brief

Windborne Systems

Industry: Climate Tech

Website