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Machine Learning Astrophysics Jobs (NOW HIRING)

GRVTY

Data Scientist 2

Annapolis Junction, MD · On-site

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 ...

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How much do machine learning astrophysics jobs pay per hour?

As of Jun 5, 2026, the average hourly pay for machine learning astrophysics in the United States is $22.82, according to ZipRecruiter salary data. Most workers in this role earn between $19.71 and $25.48 per hour, depending on experience, location, and employer.

How do professionals in machine learning astrophysics typically collaborate with domain experts and software engineers on research projects?

In machine learning astrophysics, collaboration is key to successful research outcomes. Professionals in this field often work alongside astrophysicists to ensure that data preprocessing and model outputs align with scientific objectives, while also partnering with software engineers to implement scalable and efficient algorithms. Regular meetings, code reviews, and joint data analysis sessions are common practices, enabling seamless integration of machine learning methods with domain expertise. This multidisciplinary teamwork helps address complex challenges, ensures scientific rigor, and accelerates the development of innovative solutions.

Will AI replace astronomy?

As a Machine Learning Astrophysics professional, AI is a tool that enhances data analysis and discovery in astronomy but is unlikely to fully replace human astronomers. AI automates tasks like data processing and pattern recognition, allowing scientists to focus on interpretation and theory development. Skills in programming, data analysis, and domain knowledge remain essential in this evolving field.

What is the difference between Machine Learning Astrophysics vs Data Scientist in Astrophysics?

AspectMachine Learning AstrophysicsData Scientist in Astrophysics
Required CredentialsPhysics or astrophysics degree, programming skills, machine learning knowledgeStatistics, computer science, or physics degree, programming, data analysis skills
Work EnvironmentResearch institutions, observatories, universitiesResearch projects, data analysis teams in academia or agencies
Industry UsageDeveloping models for astrophysical phenomenaAnalyzing astrophysical data sets for insights

Both roles involve data analysis and programming skills, but Machine Learning Astrophysics focuses on applying machine learning techniques to understand astrophysical phenomena, while Data Scientist in Astrophysics emphasizes broader data analysis and statistical methods within astrophysics research.

How much does NASA pay astrophysicists?

NASA astrophysicists are typically employed as federal government scientists, with salaries based on the General Schedule (GS) pay scale. Entry-level astrophysicists usually earn between GS-11 and GS-12, approximately $55,000 to $90,000 annually, with higher salaries for more experienced roles or those with advanced degrees and specialized skills. Salaries can also include benefits such as health insurance and retirement plans.

What is machine learning astrophysics?

Machine learning astrophysics is an interdisciplinary field that applies machine learning methods to solve complex problems in astrophysics. This involves using algorithms and statistical models to analyze vast amounts of astronomical data, identify patterns, classify celestial objects, and make predictions about cosmic phenomena. Researchers in this area work on projects such as detecting exoplanets, classifying galaxies, or predicting stellar evolution, often leveraging large datasets from telescopes and simulations. The integration of machine learning helps accelerate discoveries by automating data analysis and uncovering insights that would be difficult to find using traditional approaches.

What are the key skills and qualifications needed to thrive as a Machine Learning Astrophysics professional, and why are they important?

To thrive as a Machine Learning Astrophysics professional, you need a strong background in astrophysics, mathematics, and computer science, often supported by a relevant advanced degree such as a PhD. Proficiency with programming languages (like Python), machine learning frameworks (such as TensorFlow or PyTorch), and data analysis tools is essential. Critical thinking, problem-solving, and effective collaboration are vital soft skills for working with interdisciplinary teams and interpreting complex data. These capabilities are crucial for advancing research, uncovering new astrophysical phenomena, and efficiently analyzing large astronomical datasets.
Machine Learning Scientist - AI Trainer

Machine Learning Scientist - AI Trainer

DataAnnotation

Juneau, AK • On-site, Remote

$40/hr

Full-time

Posted 24 days ago

Job description

Join the DataAnnotation team and contribute to developing cutting-edge AI systems, while enjoying the flexibility of remote work and setting your own schedule. We are looking for experienced quantitative professionals to help advance AI development. AI models are increasingly capable of performing complex analytical and scientific reasoning — but these systems still need practitioners with real-world quantitative experience to validate whether the outputs actually hold up in practice.

That's where you come in. As a member of DataAnnotation's team, you'll work closely with state-of-the-art AI models on tasks like evaluating AI-generated quantitative analysis, solving technical problems, and providing feedback that directly shapes how these systems reason about data, models, and scientific problems. Whether your background is in data science, astrophysics, economics, biostatistics, operations research, or any other quantitative field, if you think rigorously about data and models, your skills are directly applicable here.

Some team members fit this work alongside a full-time role, while others treat it as their primary focus. To get started, once you sign up for an account, you'll take a short assessment (this serves as our version of an interview). If you pass, you'll receive an email confirmation, and paid work will become available on our platform.

Benefits Fully remote: work from anywhere in the US, Canada, UK, Ireland, Australia, and New Zealand. Flexible schedule: choose which projects you take on and when you work. Competitive pay: projects are paid hourly, starting at $40+ USD per hour.

Impact: help shape the future of AI systems built to reason about data and analytics. Responsibilities Evaluate AI-generated quantitative work, including statistical analysis, predictive modeling, scientific reasoning, and data-driven insights, for technical accuracy and real-world validity. Design and solve quantitative problems used to train and benchmark AI systems, spanning areas like forecasting, experimental analysis, optimization, and statistical inference.

Write clear technical explanations and well-documented analytical code. Provide feedback that directly shapes the next generation of AI models built for quantitative reasoning. Qualifications 2+ years of hands‐on experience in a quantitative role or research environment — such as data science, statistics, economics, finance, physics, biology, epidemiology, operations research, or any adjacent field.

Some coding experience required, with comfort writing and reviewing analytical code end-to-end. Practical experience with statistical methods, predictive modeling, and experiment design (e.g., A/B testing, hypothesis testing, regression, classification, time‐series forecasting). Fluency in English (native or bilingual level) with strong writing skills.

A bachelor's degree in a quantitative field is preferred (Statistics, Computer Science, Mathematics, Engineering, or similar); a master's or PhD is a plus. Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML certifications, or equivalent demonstrated expertise). Note: Payment is made via PayPal.

We will never ask for any money from you. This job is only available to those in the US, Canada, UK, Ireland, Australia, and New Zealand. #datascience #J-18808-Ljbffr

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