Machine Learning Engineer, Atmospheric Science, Data Science

DeVine provides technical and scientific support to government clients in Oceanography & Atmospheric Science among other technical disciplines.

Our company is looking for a Machine Learning Engineer , with experience in Atmospheric Science and Machine Learning (ML), to join DeVine in a full time capacity.

This position will be supporting a government customer, hence only US Citizens may be considered for hire.

Candidates who meet/exceed every requirement may be considered for remote work.

DeVine contributes to projects in data modeling, remote sensing & machine learning.

We collaborate with our clients in scientific analysis of the Earth’s atmosphere & ocean and land surfaces, as well as astronomy and astrometry.

We help our clients test and operate space-based, air-based, subsurface, and land and ocean surface-based sensors.

The successful hire will contribute to improvements in weather forecast performance to deliver more accurate weather insights to our customers.

If your experience is relevant to the requirements below, and you’d enjoy working in Silver Spring, MD, then please apply!

• Conduct innovative research at the intersection of weather prediction and machine learning, including approaches that leverage observations from satellite constellation
• Develop, verify, and document forecast improvements that provide measurable value to customers
• Partner with engineering and product teams to transition research advances into scalable, operational systems
• Communicate results through internal reviews, customer discussions, and, where appropriate, conferences or publications
• Contribute broadly to improving forecasts and overall product performance

Required experience and credentials:

• Graduate degree in atmospheric science, meteorology, computer science, or a related field
• 4+ years of experience developing ML models for weather applications
• Strong ML engineering fundamentals, including model training, validation, evaluation, and documentation
• Training, running, and verifying AI-based weather prediction models