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Machine Learning Astronomy Jobs in Reston, VA (NOW HIRING)

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$26.5K

$44.3K

$91.6K

How much do machine learning astronomy jobs pay per year?

As of Jun 4, 2026, the average yearly pay for machine learning astronomy in Reston, VA is $44,302.00, according to ZipRecruiter salary data. Most workers in this role earn between $33,800.00 and $47,900.00 per year, depending on experience, location, and employer.

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.

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 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 popular job titles related to Machine Learning Astronomy jobs in Reston, VA? For Machine Learning Astronomy jobs in Reston, VA, the most frequently searched job titles are:
What job categories do people searching Machine Learning Astronomy jobs in Reston, VA look for? The top searched job categories for Machine Learning Astronomy jobs in Reston, VA are:
What cities near Reston, VA are hiring for Machine Learning Astronomy jobs? Cities near Reston, VA with the most Machine Learning Astronomy job openings:
Machine Learning Astronomy jobs near you
Infographic showing various Machine Learning Astronomy job openings in Reston, VA as of May 2026, with employment types broken down into 8% Internship, 55% Full Time, 29% Part Time, and 8% Nights. Highlights an 55% Physical, 1% Hybrid, and 44% Remote job distribution, with an average salary of $44,302 per year, or $21.3 per hour.

Project #6 - Postbaccalaureate Positions in Astrophysics Science and Solar System Exploration Div...

CRESST2

Greenbelt, MD • On-site

Other

This job post has expired today. Applications are no longer accepted.

Job description

2026 CRESST II Postbaccalaureate Positions at NASA GSFC

Applications are now being accepted for short-term postbaccalaureate research positions to support the Astrophysics Science and Solar System Exploration Divisions at NASA/Goddard Space Flight Center (NASA/GSFC) in Greenbelt, MD. The position is funded by Southeastern Universities Research Association (SURA) through the Center for Research and Exploration in Space Science and Technology II (CRESST II).

The Astrophysics Science Division conducts a broad program of research in astronomy, astrophysics, and fundamental physics. Individual investigations address issues such as the nature of dark matter and dark energy, which planets outside our solar system may harbor life, and the nature of space, time, and matter at the edges of black holes.

The Solar System Exploration Division conducts theoretical and experimental research to explore the solar system and understand the formation and evolution of planetary systems. Laboratories within the division investigate areas as diverse as astrochemistry, planetary atmospheres, geochemistry, geophysics, geodynamics, space geodesy, extrasolar planetary systems, and comparative planetary studies.

Positions available within the Astrophysics Science and Solar System Exploration Divisions span a variety of research areas. Successful candidates will be chosen to work on one of the research projects listed below:

  • Project #1 - Cosmic dust is essential to star and planet formation, cosmic chemistry, galaxy evolution, and even cosmology. Dust forms in supernova remnants or the cool atmospheres of giant stars, but somehow half of all the dust in the Universe manages to escape the galaxies where it formed and wanders the intergalactic voids. This project will use new and existing X-ray, ultraviolet, and optical images and spectra from space-based telescopes to understand how dust survives expulsion from galaxies. The successful applicant will be expected to measure the properties of dust in galactic winds, interpret those measurements using physical principles and basic statistics, and publish their findings. Experience with basic computer programming (especially in python), basic statistics, and some background in physical science is preferred. The selected candidate will work with Dr. Edmund Hodges-Kluck and Dr. Erin Boettcher.
  • Project #2 - This project will use new and existing high-resolution X-ray spectroscopic data from the recently launched XRISM observatory, supplemented by X-ray imaging from Chandra and XMM-Newton, to derive gas velocities in merging galaxy clusters, in order to understand the geometry and power budget of these most energetic collisions in the Universe. Experience with basic computer programming, UNIX, basic statistics, and some background in physical science is preferred. The selected candidate will work with Dr. Maxim Markevitch and Dr. Cicely Potter.
  • Project #3 - You will be joining an active and collaborative research team at the forefront of theoretical and computational astrophysics. Our group specializes in modeling neutron stars and pulsars using multiwavelength observational data, primarily from Fermi and NICER, paired with advanced simulations and inference frameworks. We foster a dynamic environment where postbacs, graduate students, postdocs, and senior scientists work together on cutting-edge problems in relativistic astrophysics. In this role, you will contribute to efforts exploring the extreme physics of neutron stars and pulsars, leveraging observational data alongside state-of-the-art computational tools, including relativistic magnetohydrodynamics (MHD), particle-in-cell (PIC), and radiation transport codes, as well as statistical frameworks such as Markov Chain Monte Carlo (MCMC) and machine learning for efficient parameter estimation and model emulation. Projects include modeling pulsar particle acceleration and inferring neutron star parameters from observational data. Strong computational skills are essential; while experience with lower-level programming languages such as Fortran or C is a plus, it is not required, and candidates with proficiency in other languages (e.g., Python) are also encouraged to apply. The selected candidate will be working with Dr. Konstantinos Kalapotharakos.
  • Project #4 - Exoplanet spectroscopy modeling in support of the Habitable Worlds Observatory (HWO), and development of the open-access exoplanet software database called the Exoplanet Modeling & Analysis Center (EMAC). The selected candidate will work with members of both the HWO and EMAC teams. For the HWO work, the candidate tasks include running and analyzing simulated spectroscopic observations of potentially habitable exoplanets to determine detectability thresholds for various Astro biologically relevant molecules. For the EMAC work, the candidate will seek out and recruit new exoplanet-related software to the repository. They will also perform curation tasks improving the metadata of information already on EMAC as well as making code modifications to the Django-based service. The selected candidate will work with Dr. Avi Mandell.
  • Project #5 - The Sellers Exoplanet Environments Collaboration (SEEC) connects multi-disciplinary researchers from the Planetary, Earth, Astrophysics, and Heliophysics science divisions at NASA GSFC to study exoplanet atmospheres and climates. Using a wide range of scientific and technical resources, SEEC scientists inform current NASA exoplanet observations and prepare for future missions. SEEC is posting a general call for candidates interested in supporting a project to be determined in one of two areas: observational, theory-centered or experiment-centered. Programming experience (especially Python) is a plus for the observational and theory-centered project. Instrumentation experience is a plus for the experiment-centered projects. The selected candidate will work with a to be determined SEEC team member.
  • Project #6 - Superconducting cryogenic detectors are enabling transformative science that will address fundamental questions about our Universe. This project supports development of next-generation transition-edge sensor (TES) bolometers, kinetic inductance detectors (KIDs), and on-chip spectrometers for cosmic microwave background (CMB), line intensity mapping (LIM), and a broad range of astrophysics observations. You will be joining an active, collaborative group responsible for developing and testing on-chip spectrometers for the balloon-borne Experiment for Cryogenic Large-Aperture Intensity Mapping (EXCLAIM), KID readout electronics for the Probe Far-Infrared Mission for Astrophysics (PRIMA), and TES polarimeters for CMB observations from the ground (with the Cosmology Large Angular Scale Surveyor, CLASS) and space (LiteBIRD, CMB Probe). The successful candidate will be responsible for operating cryogenic testbeds, carrying out tests of detectors with and without optical signals, and analyzing the data from the tests. The selected candidate will work with Dr. Thomas Essinger-Hileman.

Candidates should be soon or recent graduates with a bachelor's degree in astronomy, physics, computer science, mathematics, chemistry, or a related science, or engineering discipline. Individuals who have already earned a master's degree may apply for Project #3 only. We encourage applicants who are considering applying to a graduate program in the near future, and who wish to expand their research experience in the interim to also apply.

To apply, each applicant should submit a cover letter describing personal background and interest in the applicants' chosen project(s), Curriculum Vitae, unofficial transcript, and contact information for two references to each project of interest through the CRESST II Breezy application platform. After sponsors review applications, additional support materials may be requested which may include a work sample in the form of a report, poster, journal article, writing sample, or coding examples. The deadline for Project #6 only is Wednesday, May 27, 2026, at 11:59 pm EDT. Please submit an application for this project by this date.

This is a full-time position working on-site at NASA/GSFC for 40 hours/week. The position offers a competitive annual salary of $48,900, along with comprehensive benefits. The desired start date ranges from August - September 2026, with an initial employment term of one year that may be extended for one additional year contingent on performance and availability of funds. For general questions, contact CRESST II Special Programs Manager Aaliyah Kerr at aaliyah.kerr (at)nasa.gov. SURA is an equal opportunity employer and welcomes all to apply. EOE/M/F/D/V.

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