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Machine Learning Astronomy Jobs in Maryland (NOW HIRING)

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Machine Learning Astronomy information

What is machine learning astronomy?

Machine learning astronomy is the application of machine learning techniques to analyze and interpret astronomical data. This field combines computer science, statistics, and astronomy to automate tasks such as classifying celestial objects, detecting anomalies, and predicting astronomical events. With the increasing volume of data from telescopes and space missions, machine learning helps astronomers process and extract meaningful insights more efficiently. Researchers in this area develop algorithms that can learn patterns from vast datasets, leading to new discoveries and a deeper understanding of the universe.

What are some common challenges faced by professionals working in machine learning astronomy?

Machine learning astronomers often encounter challenges such as handling extremely large and complex datasets, ensuring data quality, and effectively preprocessing astronomical data to reduce noise and artifacts. Additionally, interpreting model results in a scientific context can be demanding, as it requires both technical expertise and domain knowledge. Collaboration with astronomers, data engineers, and software developers is essential to ensure that machine learning models are both accurate and scientifically meaningful.

What are the key skills and qualifications needed to thrive as a machine learning astronomer, and why are they important?

To thrive as a Machine Learning Astronomer, you need a strong background in astrophysics, statistical analysis, and programming (often with a PhD in a related field). Proficiency with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and astronomical data systems is essential. Critical thinking, problem-solving, and effective collaboration are key soft skills for innovating solutions and working within research teams. These skills enable the effective analysis of large astronomical datasets, driving new discoveries and advancements in the field.

What is the difference between Machine Learning Astronomy vs Data Scientist?

AspectMachine Learning AstronomyData Scientist
Required CredentialsDegree in Astronomy, Physics, or related fields; knowledge of machine learningDegree in Computer Science, Statistics, or related fields; strong programming skills
Work EnvironmentResearch institutions, observatories, academiaCorporate, tech companies, consulting firms
Industry UsageAnalyzing astronomical data, developing models for celestial phenomenaBusiness analytics, predictive modeling, data visualization

Machine Learning Astronomy focuses on applying machine learning techniques to astronomical data within research settings, while Data Scientists work across various industries analyzing data to inform business decisions. Both roles require strong analytical skills and programming knowledge but differ in domain focus and work environment.

How is machine learning used in astronomy?

Machine learning astronomy involves applying algorithms to analyze large datasets from telescopes and space missions, enabling tasks such as identifying celestial objects, classifying galaxies, detecting exoplanets, and predicting cosmic phenomena. Professionals in this field often use tools like Python, TensorFlow, and data analysis techniques to interpret complex astronomical data efficiently.

What are popular job titles related to Machine Learning Astronomy jobs in Maryland?

For Machine Learning Astronomy jobs in Maryland, the most frequently searched job titles are:

What job categories do people searching Machine Learning Astronomy jobs in Maryland look for?

The top searched job categories for Machine Learning Astronomy jobs in Maryland are:

What cities in Maryland are hiring for Machine Learning Astronomy jobs?

Cities in Maryland with the most Machine Learning Astronomy job openings:

Machine Learning Astronomy jobs near you
Infographic showing various Machine Learning Astronomy job openings in Maryland as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 22% Part Time, 1% Temporary, and 3% Contract. Highlights an 89% Physical, 2% Hybrid, and 9% Remote job distribution.

MS, PhD, Research Scientist, Machine Learning, Atmospheric Science

Silver Spring, MD • On-site

DeVine
See more jobs at DeVineexternal

$120K - $150K/yr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 5 days ago

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 Research Scientist, with experience in Atmospheric Science and Machine Learning (ML), to join DeVine in a full time capacity. 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.

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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