Overview

The AiTHENA Program supports NASA researchers by pairing high-impact technical projects with AIML talent to accelerate mission-relevant capability development. We are seeking an AIML Research Associate  to contribute to applied machine learning, data workflows, and prototype development across NASA research projects (e.g., digital twin enablement, predictive analytics, automation, and decision support tools).

This role is designed for someone who can operate in a research environment: translate ambiguous technical needs into tractable AIML tasks, build reproducible prototypes, and communicate results clearly to technical stakeholders.

Salary range for this role is $$87,000-$97,000 commensurate with education and experience. Remote position.

What You’ll Do

• 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.

Required Qualifications

• 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

Preferred Qualifications

• 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 inWest Virginia, NASA Ames Research Center in Mountain View, Ca, or NASA HQ in Washington, D.C.