About this opportunity
DeVine Consulting, Inc. lists this MS, PhD, Atmospheric Science, Data Science, Machine Learning Engineer opportunity in silver spring, Maryland. Review the employer’s description below for duties, qualifications and application requirements.
Job description
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.
Duties:
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 forecastsand 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
Working in cloud-based computing environments
Handling large meteorological datasets and common data formats at scale
Modern deep learning frameworks (e.g., PyTorch or TensorFlow)
Large geophysical dataset formats (GRIB, NetCDF, ZARR)
Proficiency with deep learning frameworks (e.g., PyTorch, TensorFlow)
Familiarity with cloud-based computing environments (AWS, GCP, Azure)
Strong written and verbal communication skills
Ability to manage multiple projects and balance competing priorities
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.
Duties:
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 forecastsand 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
Working in cloud-based computing environments
Handling large meteorological datasets and common data formats at scale
Modern deep learning frameworks (e.g., PyTorch or TensorFlow)
Large geophysical dataset formats (GRIB, NetCDF, ZARR)
Proficiency with deep learning frameworks (e.g., PyTorch, TensorFlow)
Familiarity with cloud-based computing environments (AWS, GCP, Azure)
Strong written and verbal communication skills
Ability to manage multiple projects and balance competing priorities
About the position:
Position Type: Full-time, Must be U.S. Citizen
Location: Silver Spring, MD
Benefits: Medical, Dental, Vision, 401K, Life Insurance, Paid Holidays, Paid Sick Leave and Paid Vacation
Compensation: $120K to $150K per year salary range DOE and skills
Equal Opportunity Employer We are committed to a policy of assuring that all applicants for employment are recruited, hired and assigned on the basis of qualifications and merit without discrimination based on any protected classification, including, but not limited to, race, color, religion, sex, sexual orientation, national origin, veteran status, age, disability, handicap, marital status, or any other characteristic protected by applicable laws.
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Worksite address
silver spring, MD, 20900, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.