Machine Learning Nasa information
See Washington, DC salary details
$22.5K - $41.3K
15% of jobs
$41.3K - $60.1K
9% of jobs
$65.8K is the 25th percentile. Wages below this are outliers.
$60.1K - $78.9K
5% of jobs
$78.9K - $97.7K
9% of jobs
$97.7K - $116.5K
10% of jobs
The median wage is $122.1K / yr.
$116.5K - $135.3K
11% of jobs
$135.3K - $154.1K
12% of jobs
$163.5K is the 75th percentile. Wages above this are outliers.
$154.1K - $172.9K
12% of jobs
$172.9K - $191.6K
10% of jobs
$191.6K - $210.4K
6% of jobs
$210.4K - $229.2K
3% of jobs
How much do machine learning nasa jobs pay per year?
As of Aug 13, 2026, the average yearly pay for machine learning nasa in Washington, DC is $125,621.00, according to ZipRecruiter salary data. Most workers in this role earn between $69,788.00 and $169,639.00 per year, depending on experience, location, and employer.
Machine Learning specialists at NASA use advanced algorithms and data analysis techniques to help solve complex problems in space exploration and research. Their work includes developing models for spacecraft navigation, analyzing satellite imagery, predicting equipment failures, and supporting scientific discoveries through data-driven insights. By leveraging artificial intelligence and machine learning, they help NASA make more accurate predictions, automate processes, and enhance mission safety and efficiency.
To thrive as a Machine Learning Scientist at NASA, you need a solid background in computer science, mathematics, and statistics, typically supported by an advanced degree in a related field. Proficiency with programming languages like Python or R, familiarity with machine learning libraries (such as TensorFlow or PyTorch), and experience with large-scale data analysis are essential. Strong problem-solving skills, creativity, and the ability to collaborate across multidisciplinary teams help set top candidates apart. These skills and qualities are crucial for developing innovative AI solutions that support NASA’s scientific missions and research objectives.
Machine learning professionals at NASA often work with complex, high-dimensional datasets collected from space missions, satellites, and simulations, which can present unique challenges such as data sparsity, noise, and the need for robust, interpretable models. Collaboration with scientists and engineers from diverse backgrounds is frequent, requiring excellent communication skills to translate technical findings into actionable insights. To prepare, applicants should familiarize themselves with domain-specific data, stay updated on the latest advancements in machine learning, and practice interdisciplinary teamwork to effectively contribute to NASA's innovative projects.
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