AIML Research Associate
- Collaborate with NASA project mentors and technical teams to define AIML problem statements, success metrics, and validation plans.
- Develop and evaluate ML models (e.g., regression/classification, time series, anomaly detection, NLP, computer vision—based on project needs).
- Build reproducible data pipelines for ingestion, cleaning, feature engineering, labeling, and dataset versioning.
- Prototype and deliver proof-of-concept tools (scripts, notebooks, small applications, dashboards, or APIs) that can be transitioned to the mentor team.
- Apply sound practices for experimentation: baselines, ablation studies, cross-validation, and uncertainty/error analysis.
- Document methods, assumptions, limitations, and recommendations in clear technical writeups.
- Contribute to best practices for trustworthy/robust ML (data leakage prevention, bias checks, model monitoring considerations, and traceability).
- Participate in AiTHENA technical exchanges, demos, and closeout deliverables.
- Bachelor’s degree (or higher) in Computer Science, Engineering, Applied Math, Data Science, Physics, or a related field—or equivalent demonstrated experience.
- Demonstrated experience building AIML models and evaluating performance using appropriate metrics.
- Proficiency with Python and common data/ML tooling (e.g., NumPy, pandas, scikit-learn; familiarity with PyTorch or TensorFlow is a plus).
- Experience working with real-world datasets (messy data, missing values, outliers, labeling challenges).
- Ability to communicate technical content clearly (documentation, presentations, or technical memos).
- Strong organizational skills and the ability to manage tasks independently in a fast-paced research environment.
- U.S. Citizenship or Permanent Residency required for in-person positions
- Remote positions are open to all those authorized to work in the U.S.
- Experience with one or more of the following:
- Time series modeling, forecasting, and anomaly detection
- NLP (document classification, information extraction, RAG-style workflows)
- Computer vision (segmentation, detection, image enhancement)
- Physics-informed ML or surrogate modeling
- Experiment design and uncertainty quantification
- Familiarity with software engineering practices: Git-based workflows, unit testing, code review, packaging; Containerization (Docker) and/or workflow automation
- Experience with cloud/HPC environments and MLOps concepts (CI/CD, model versioning, monitoring) is a plus.
- Exposure to “high assurance” or safety/mission-relevant development practices (traceability, verification, controlled environments).
- Graduate students enrolled in a PhD or Master's program in Computer Science, Data Science, or related fields or career transitioners/ early career professionals
- Living within a reasonable commuting distance from NASA center for project selected for: Langley Research Center in Hampton, VA NASA Katherine Johnson IV&V Facility in West Virginia, NASA Ames Research Center in Mountain View, Ca, or NASA HQ in Washington, D.C.
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