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Machine Learning Astronomy Jobs (NOW HIRING)

Socket.dev

Senior Dev Ops Engineer

Manhattan, NY · On-site

$160 - $220/hr

We run Apache Airflow on Astronomer with DAGs that orchestrate high-volume processing across AWS and Kubernetes, including machine learning inference inside pipeline tasks. You will build the ...

Treeswift Inc

Senior Dev Ops Engineer

New York, NY · On-site

$142K - $182K/yr

We run Apache Airflow on Astronomer with DAGs that orchestrate high-volume processing across AWS and Kubernetes, including machine learning inference inside pipeline tasks. You will build the ...

Treeswift Inc

Senior Dev Ops Engineer

Manhattan, NY · On-site

$160 - $220/hr

We run Apache Airflow on Astronomer with DAGs that orchestrate high-volume processing across AWS and Kubernetes, including machine learning inference inside pipeline tasks. You will build the ...

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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 Aug 25, 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 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.
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What states have the most Machine Learning Astronomy jobs?

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Machine Learning Astronomy jobs near you
Infographic showing various Machine Learning Astronomy job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Postdoctoral Research Associate in Particle Astrophysics (Gaitskell)

Brown University

Providence, RI • On-site

Full-time

Re-posted 10 hours ago

Brown University rating

8.1

Company rating: 8.1 out of 10

Based on 27 frontline employees who took The Breakroom Quiz

168th of 622 rated colleges and universities

Job description

Description
The Particle Astrophysics Group in the Department of Physics at Brown University will have an opening for a postdoctoral research associate starting August 1, 2025 or earlier if desired. Timing can be negotiated. The position will involve working on the LUX ZEPLIN (LZ) dark matter search experiment, on the applications of machine learning in physics data analysis, and on new photodetector development.
Details of the research programs and the members of the Brown Particle Astrophysics Group are shown at https://particleastro.brown.edu. The group is led by Prof. Rick Gaitskell and is focused on experimental searches for dark matter. Brown is a major group in the world-leading LZ 8-tonne liquid xenon TPC direct detection experiment that is currently operating underground at Sanford Lab. Detector operations and follow-up data analysis are expected to extend into 2027.
The research will include dark matter search data analysis, nuclear recoil detector calibration techniques including the use of a deuterium-deuterium accelerator source, photodetector development for next-generation experiments, and also machine learning applied in a range of physics analyses. Previous experience with noble liquid detectors, direct dark matter search experiments, photodetectors, low-background techniques, data analysis, machine learning, or Monte Carlo simulations (GEANT4) will be advantageous. We are also looking at developing future small/fast satellite missions in particle astrophysics. There are no teaching responsibilities associated with this position.
The Brown University Department has a very active program in experimental and theoretical Astrophysics, Particle Astrophysics, Cosmology, and Particle Physics.
Applications should be submitted by December 1, 2024 for full consideration, although review of applications will continue on a rolling basis until the position is filled. Any inquiries should be sent to Particleastro_postdoc@brown.edu. Submission is made online using http://apply.interfolio.com/116965.
Brown University seeks to recruit and retain a diverse workforce to maintain the excellence of the University and to offer our students richly varied disciplines, perspectives, viewpoints, and ways of knowing and learning.
Qualifications
Initial offers will be made for one year, with the potential for renewal for a further two years. The successful applicant must have completed the requirements for a Ph.D. or equivalent qualification in physics, astrophysics, computer science, or a related disciple prior to the start of the appointment.
Application Instructions
Interested candidates should submit the following application materials:
- Curriculum vitae.
- Statement of research interests. The statement of research interests should not exceed 3 pages, excluding the bibliography.
- Three letters of recommendation submitted prior to the application deadline.
Applicants should state in their cover letter how, through their research approaches and/or public engagement, they are prepared to advance Brown's strong commitment to diversity, equity, and inclusion.

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