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Machine Learning Astronomy Jobs (NOW HIRING)

GRVTY

Data Scientist 2

Annapolis Junction, MD

This role combines artificial intelligence and machine learning skills with a strong foundation in ... astronomy), or other science disciplines with a substantial computational component (i.e ...

University of Wisconsin-Madison

Scientist I

Madison, WI · On-site

$60K - $80K/yr

Training and/or published research in data science & machine learning * Domain expertise in high-energy physics and/or astrophysics Education: PhD in physics or related fields (especially computer ...

University of Wisconsin-Madison

Scientist I

Madison, WI · On-site +1

$60K - $80K/yr

Training and/or published research in data science & machine learning * Domain expertise in high-energy physics and/or astrophysics Education: PhD in physics or related fields (especially computer ...

Wisconsin

Scientist I

Madison, WI

$60K - $80K/yr

Training and/or published research in data science & machine learning * Domain expertise in high-energy physics and/or astrophysics Education: PhD in physics or related fields (especially computer ...

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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 Jun 24, 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 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.

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.
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 June 2026, with employment types broken down into 83% Full Time, and 17% Part Time. Highlights an 100% In-person job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Data Scientist IV with Security Clearance

Black Eagle Defense

Fort George G Meade, MD • On-site

$190K - $247K/yr

Other

Posted 18 days ago

Job description

Job Description SALARY RANGE $190,000 - $247,000/year DUTIES As a successful candidate for the Data Scientist IV role, you will devise strategies for extracting meaning and value from large datasets and serve as an established AI expert with demonstrable experience designing and implementing sophisticated AI/ML and data science solutions across various domains within the MPO customer environment. You will make and communicate principled conclusions from data using elements of mathematics, statistics, computer science, and application-specific knowledge. Through analytic modeling, statistical analysis, programming, and other appropriate scientific methods, you will develop and implement qualitative and quantitative approaches for characterizing, exploring, and assessing large datasets in various states of organization, cleanliness, and structure, accounting for the unique features and limitations inherent in NSA/CSS data holdings. You will design and implement Agentic AI systems and customized Retrieval Augmented Generation (RAG) capabilities, assess and select appropriate machine learning models for specific mission purposes, and optimize model performance across constrained environments. You will generate, evaluate, train, and optimize machine learning models, including large language models (LLMs), for deployment in low-memory, low-resource edge device environments. You will translate practical mission needs and analytic questions into technical requirements, assist others in drawing appropriate conclusions from analytical results, effectively communicate complex technical information to non-technical audiences, and make informed recommendations regarding competing technical solutions by maintaining awareness of evolving NSA/CSS collection, processing, storage, and analytic capabilities and limitations. Required Skills SKILLS Employ some combination (2 or more) of the following skill areas: * 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, AI/ML model selection and optimization) QUALIFICATIONS Education and Experience Bachelor's degree with 15 years of relevant experience, or
  • Associate's degree with 17 years of relevant experience
Experience must be in-depth and clearly related to the position Degree Requirements Bachelor's degree in one of the following:
  • Mathematics
  • Applied Mathematics
  • Statistics or Applied Statistics
  • Machine Learning
  • Data Science
  • Operations Research
  • Computer Science
  • Related degrees may be considered, including:
  • Computer Information Systems
  • Engineering
  • Physical or hard science degrees (e.g., physics, chemistry, biology, astronomy) or other science disciplines may be considered if they include a substantial computational component and at least five advanced (300-level or higher) courses in:
  • Mathematics (e.g., linear algebra, probability, statistics, machine learning), and/or
Computer Science (e.g., algorithms, programming, data structures, data mining, artificial intelligence) Additional Degree Considerations College-level requirements or upper-level math courses designated as elementary or basic do not count
A broader range of degrees may be considered if accompanied by a Certificate in Data Science from an accredited college or university Required Experience Areas Relevant experience must include work in the following areas: Designing and implementing machine learning and AI solutions
  • Data science
  • Advanced analytical algorithms
  • Programming in at least one high-level language (e.g., Python)
  • Statistical analysis (e.g., variability, sampling error, inference, hypothesis testing, exploratory data analysis, linear models)
  • Data management (e.g., data cleaning and transformation)
  • Data mining
  • Data modeling and assessment
  • Artificial intelligence
  • Software engineering
Experience spanning more than five of the areas listed above is strongly preferred

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