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Machine Learning Astronomy Jobs (NOW HIRING)

Gormat

Data Scientist 2

Annapolis Junction, MD · On-site

Gormat is seeking a Level 2 Data Scientist with expertise in AI, Machine Learning, and Natural ... astronomy), or other science disciplines with a substantial computational component (i.e ...

Gormat

Data Scientist 2

Annapolis Junction, MD · On-site

$99K - $114K/yr

You will also provide advanced discovery support using machine learning, analytical prototyping ... astronomy), or other science disciplines with a substantial computational component (i.e ...

Gormat

Data Scientist 2

Annapolis Junction, MD · On-site

$99K - $114K/yr

You will also provide advanced discovery support using machine learning, analytical prototyping ... astronomy), or other science disciplines with a substantial computational component (i.e ...

Gormat

Data Scientist 2

Annapolis Junction, MD

You will also provide advanced discovery support using machine learning, analytical prototyping ... astronomy), or other science disciplines with a substantial computational component (i.e ...

The Pennsylvania State University

D. in Astronomy, Physics, or a related field is required. Experience with HPC systems, machine learning, and GRB monitor data analysis would be an advantage. Additional Information Applications must ...

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All JobsMachine Learning Astronomy Jobs

Machine Learning Astronomy information

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$25.5K

$42.6K

$88K

How much do machine learning astronomy jobs pay per year?

As of Jul 14, 2026, the average yearly pay for machine learning astronomy in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

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.

What are 5 potential jobs for astronomy?

Potential jobs for astronomy graduates include research scientist at observatories or universities, data analyst for space agencies, astrophysics researcher, science communicator or educator, and software developer for astronomical data analysis. These roles often require strong analytical skills, programming knowledge, and familiarity with telescopes or data processing tools.

How much do machine learning engineers make at NASA?

Machine learning engineers at NASA typically earn between $90,000 and $150,000 annually, depending on experience, education, and security clearance levels. Salaries may also vary based on location and specific project responsibilities, with some roles requiring expertise in data analysis, programming, and scientific computing tools.

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 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.

Does NASA have machine learning engineers?

NASA employs machine learning engineers to develop algorithms for data analysis, spacecraft navigation, and scientific research. These roles often require expertise in programming, data science, and tools like Python and TensorFlow, with positions available through federal job portals and NASA's career website.

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.

Can AI replace astronomers?

Machine Learning Astronomers use AI to analyze large datasets, identify patterns, and make predictions about celestial phenomena. While AI can automate data processing and assist in research, it does not replace the need for human expertise in designing experiments, interpreting results, and making scientific judgments. The role of astronomers remains essential for guiding AI applications and advancing understanding of the universe.
More about Machine Learning Astronomy jobs
What cities are hiring for Machine Learning Astronomy jobs? Cities with the most Machine Learning Astronomy job openings:
What states have the most Machine Learning Astronomy jobs? States with the most job openings for Machine Learning Astronomy jobs include:
What job categories do people searching Machine Learning Astronomy jobs look for? The top searched job categories for Machine Learning Astronomy jobs are:
Machine Learning Astronomy jobs near you
Infographic showing various Machine Learning Astronomy job openings in the United States as of July 2026, with employment types broken down into 2% Locum Tenens, 81% As Needed, 7% Full Time, 7% Part Time, and 3% Summer. Highlights an 64% Physical, 1% Hybrid, and 35% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Data Scientist 2

Gormat

Annapolis Junction, MD • On-site

Full-time

Posted 7 days ago

Job description

Job Summary:
Gormat is seeking a Level 2 Data Scientist with expertise in AI, Machine Learning, and Natural Language Processing. The role involves developing large language datasets, rapid prototyping, and data mining while leveraging advanced statistical and predictive modeling techniques.
Responsibilities:
• We are seeking a Data Scientist with a background in AI, Machine Learning (ML), and Natural Language Processing (NLP). You will triage the development of large language datasets and develop tools and techniques for analysis. The focus of this position will be on rapid prototyping and data mining.
• The Level 2 Data Scientist shall possess the following capabilities: Foundations: (Mathematical, Computational, Statistical).
• Data Processing: (Data management and curation, data description and visualization, workflow and reproducibility).
• Modeling, Inference, and Prediction: (Data modeling and assessment, domain-specific considerations).
• Ability to make and communicate principal conclusions from data using elements of mathematics, statistics, computer science, and applications-specific knowledge.
• Ability to use analytic modeling, statistical analysis, programming, and/or another appropriate scientific method, develop and implement qualitative and quantitative methods for characterizing, exploring, and assessing large datasets in various states of organization, cleanliness, and structure that account for the unique feature and limitations inherent in Customer data holdings.
• Translate practical mission needs and analytic questions related to large datasets into technical requirements and, conversely, assist others with drawing appropriate conclusions from the analysis of such data.
• Effectively communicate complex technical information to non-technical audiences.
• AI/ML experience to impact and assess large datasets. This includes data modeling, computational mathematics, qualitative and quantitative techniques, data visualizations and AI/ML model development and deployment.
• Advanced statistical and predictive modeling.
• Large-scale data processing (e.g., Spark, cloud data platforms).
• Providing advanced discovery support utilizing machine learning, analytical prototyping, scripting, automation, data visualization, statistical analysis, and TechSIGINT tools. Solution needs be adaptable to new tools and technologies as needed.
Qualifications:
Required:
• Bachelor's Degree with 3 years of relevant experience, associate's degree with 5 years of experience may be considered for individuals with in-depth experience that is clearly related to the position.
• Bachelor's Degree must be in Mathematics, Applied Mathematics Statistics, Applied Statistics, Machine learning, Data Science, Operations Research, or Computer Science or a degree in a related field (Computer Information Systems, Engineering), a degree in the physical/hard sciences (e.g. physics, chemistry, biology, astronomy), or other science disciplines with a substantial computational component (i.e. behavioral, social, or life) may be considered if it included a concentration of coursework (5 or more courses) in advanced Mathematics (typically 300 level or higher, such as linear algebra, probability and statistics, machine learning) and/or computer science (e.g. algorithms, programming, data structures, data mining, artificial intelligence). College-level requirement, or upper-level math courses designated as elementary or basic do not count.
• Broader range of degrees will be considered if accompanied by a Certificate in Data Science from an accredited college/university.
• Relevant experience must be in designing/implementing machine learning, data science, advanced analytical algorithms, programming (skill in at least one high level language (e.g. Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data mining, data modeling and assessment, artificial intelligence, and/or software engineering.
• TS/SCI with polygraph is required.
Company:
Gormat is a provider of cyber security, information assurance, program management, software, and acquisition support services. Founded in 2014, the company is headquartered in Woodbine, USA, with a team of 11-50 employees. The company is currently Early Stage.

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