> Markdown version of [/jobs/ext/4172571-computer-scientist-iv-step-4](https://www.wearedevelopers.com/jobs/ext/4172571-computer-scientist-iv-step-4). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Computer Scientist IV, Step 4 - **Company:** University of Alabama - **Location:** Huntsville, AL, United States - **Experience:** Experienced - **Salary:** $102,266.0 - $108,707.0 - **Contract:** Permanent contract - **Skills:** Artificial Neural Networks, Computer Vision, Business Software, Profiling, Software Debugging, Python (Programming Language), Machine Learning, Oracle Data Service Integrator, Spatial Data Infrastructures, Large Language Models, Deep Learning, Information Technology, Data Management, Variational Autoencoders, Machine Learning Operations, Service Stack - **Published:** October 31, 2026 - **Apply:** https://www.careerbuilder.com/job-details/computer-scientist-iv-step-4-al--5f8a977f-0340-421e-8d4c-b1da2a24b428 ## About the Role Computer Science, Data Science, Technical Support, Computer Science, Spatial Data, Earth Sciences, Scientific Research, Operations Research, Data Management, Informatics, * Master's degree in Computer Science/Applied Mathematics/Physics or a related field (a Bachelor's degree and experience in a specialized area may be substituted for a degree). * Minimum of 5.5 years of full-time verifiable work experience. * Experience programming in Python or a related language. * Experience with neural deep learning methods and machine learning. Desired Qualifications: Ph.D. in Computer Science/Applied Mathematics/Physics or a related field is desired. 3+ years of experience building machine learning models for business applications is desired. Experience with LLMs or vision-based transformers and multimodal architectures is desired. Thorough understanding of AI/ML technology stack is desired. Extensive experience in CNN, Transformer and Graph Neural Network is desired. Experience with generative deep learning models applicable to the creation of synthetic data like CNNs, GANs, VAEs, etc., is desired. Experience with large-scale machine learning systems, such as profiling and debugging, and understanding of system performance and scalability is desired. Experience with cross-domain/multi-task model training including datasets from diverse sources, balancing many axes of measurement in evaluation, and deploying to multiple products is desired. Experience in publications at top-tier peer-reviewed conferences or journals is desired. Published Salary (if available) ## Description The Computer Scientist will provide geospatial and information technology assistance and specialization for the Inter-Agency Implementation and Advanced Concepts Team (IMPACT) in the Earth Systems Science Center. The candidate will be expected to work on data science issues in research and operational frameworks. The selected candidate will support the Office of Data Science and Informatics (ODSI) within the Lab for Applied Science (LAS) under the Earth System Science Center (ESSC) at UAH. This role focuses on advancing data science capabilities to support NASA Earth science data management, access, and dissemination. ODSI evaluates trends across data science and informatics communities to inform strategic direction and develop effective, scalable solutions. The office also provides a user-centered perspective on how Earth science data are represented, communicated, and utilized. The position contributes to cross- disciplinary collaboration aimed at improving all phases of the cycle, including policy, engineering, workflows, and information delivery. Duties / Responsibilities: * Leverage NASA's diverse science data sets along with large-scale computing resources to fast-track advancements in LLMs, computer vision, and multimodal systems, ensuring our Foundation model team stays at the forefront of innovation in science. * Work with a large team, including external collaborators. * Present research findings.