Application Guide

How to Apply for Machine Learning Scientist

at Tomorrow.io

🏢 About Tomorrow.io

Tomorrow.io is pioneering the integration of proprietary satellite data with machine learning to deliver hyper-accurate, customizable weather intelligence. Unlike traditional weather services, they build their own satellite constellation specifically for weather prediction, creating unique datasets. Working here means directly contributing to climate resilience solutions that help industries optimize operations against weather risks.

About This Role

This Machine Learning Scientist role focuses on developing novel deep learning approaches that leverage Tomorrow.io's satellite constellation data to improve weather forecasting. You'll bridge research and engineering by translating innovative models into operational systems that provide measurable customer value. The impact lies in creating tangible forecast improvements that enhance climate resilience for various industries.

💡 A Day in the Life

A typical day involves analyzing satellite constellation data to identify patterns for ML model improvement, collaborating with engineering teams to implement model enhancements in cloud infrastructure, and validating forecast improvements against ground truth observations. You might spend time documenting model performance metrics for customer-facing teams and participating in research discussions about novel approaches to weather prediction.

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🎯 Who Tomorrow.io Is Looking For

  • Has a graduate degree in atmospheric science or meteorology with strong ML experience, or a computer science background with proven application to geoscience problems
  • Demonstrates 2+ years specifically applying deep learning to weather prediction or closely related geoscience domains (not just general ML applications)
  • Possesses hands-on experience with large-scale meteorological datasets in cloud environments (AWS/GCP/Azure) and modern deep learning frameworks like PyTorch or TensorFlow
  • Shows ability to document and communicate technical results effectively to both technical teams and customers

📝 Tips for Applying to Tomorrow.io

1

Highlight specific projects where you applied deep learning to weather, climate, or geospatial data - quantify improvements in forecast accuracy or model performance

2

Demonstrate familiarity with satellite data processing by mentioning specific datasets (GOES, MODIS, Sentinel) or techniques you've used with remote sensing data

3

Showcase experience transitioning research models to production systems, mentioning specific cloud tools (AWS SageMaker, Google AI Platform) and scalability considerations

4

Reference Tomorrow.io's specific technology by mentioning their satellite constellation or weather intelligence platform in your application materials

5

Include examples of how you've measured and communicated the business value of ML improvements, not just technical metrics

✉️ What to Emphasize in Your Cover Letter

['Your specific experience applying deep learning to weather prediction or geoscience problems, with concrete examples', "How you've successfully partnered with engineering teams to operationalize research models in production environments", 'Your approach to working with large meteorological datasets and satellite observations at scale', "Why you're specifically interested in Tomorrow.io's satellite constellation approach versus traditional weather data sources"]

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Study Tomorrow.io's satellite constellation technology and understand what makes their data collection unique
  • → Review their customer case studies to understand how different industries use their weather intelligence
  • → Explore their technical blog and research publications to understand their current ML approaches
  • → Understand their product offerings and how machine learning integrates into their weather intelligence platform

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Walk through a specific weather prediction project where you applied deep learning - what data, models, and validation approaches you used
2 How would you design an experiment to verify forecast improvements provide measurable customer value?
3 Describe your experience processing and working with satellite imagery or remote sensing data for ML applications
4 What challenges have you faced when transitioning research models to scalable production systems, and how did you overcome them?
5 How would you approach improving Tomorrow.io's existing forecasting models using their proprietary satellite data?
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⚠️ Common Mistakes to Avoid

  • Presenting only general ML experience without specific examples in weather, climate, or geoscience domains
  • Focusing solely on research achievements without demonstrating ability to operationalize models or work with engineering teams
  • Showing no familiarity with meteorological data formats, satellite data, or the unique challenges of weather prediction ML

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

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Good luck with your application to Tomorrow.io!

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