AIML Research Associate
Analytical Mechanics Associates, Louisiana, MO, United States
AIML Researcher – NASA AiTHENA Program
Location: United States (Louisiana). Remote and in-person candidates considered. Hourly pay: $23.10 - $33.50 depending on locality.
Benefits: vision insurance, long-term disability, tuition reimbursement, and a 401(k) plan.
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 Early Career Professional (ECP) to contribute to applied machine learning, data workflows, and prototype development across NASA research projects such as digital twin enablement, predictive analytics, automation, and decision support tools.
This role is 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.
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 (regression/classification, time series, anomaly detection, NLP, computer vision) as needed.
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 transition to the mentor team.
Apply sound experimentation practices: baselines, ablation studies, cross-validation, and uncertainty/error analysis.
Document methods, assumptions, limitations, and recommendations in clear technical write-ups.
Contribute to best practices for trustworthy/robust ML, including 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.
Experience building AIML models and evaluating performance using appropriate metrics.
Proficiency with Python and common data/ML tooling (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 ability to manage tasks independently in a fast-paced research environment.
Preferred Qualifications
Experience with time series modeling, forecasting, and anomaly detection.
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 in a PhD or Master’s program in Computer Science, Data Science, or related fields; or career transitioners/early career professionals.
Living within reasonable commuting distance from selected NASA site for the project selected: Langley Research Center, NASA Katherine Johnson IV&V Facility West Virginia, NASA Ames Research Center, Mountain View CA, or NASA HQ Washington, D.C.
Citizenship Requirements
U.S. citizenship or permanent residency required for in-person positions. Remote positions open to all U.S. authorized workers.
EEO Statement
AMA is an affirmative action/equal opportunity employer and does not discriminate against any applicant for employment or employee because of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, protected veteran status, or any other characteristic prohibited under federal, state, or local laws.
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Location: United States (Louisiana). Remote and in-person candidates considered. Hourly pay: $23.10 - $33.50 depending on locality.
Benefits: vision insurance, long-term disability, tuition reimbursement, and a 401(k) plan.
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 Early Career Professional (ECP) to contribute to applied machine learning, data workflows, and prototype development across NASA research projects such as digital twin enablement, predictive analytics, automation, and decision support tools.
This role is 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.
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 (regression/classification, time series, anomaly detection, NLP, computer vision) as needed.
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 transition to the mentor team.
Apply sound experimentation practices: baselines, ablation studies, cross-validation, and uncertainty/error analysis.
Document methods, assumptions, limitations, and recommendations in clear technical write-ups.
Contribute to best practices for trustworthy/robust ML, including 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.
Experience building AIML models and evaluating performance using appropriate metrics.
Proficiency with Python and common data/ML tooling (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 ability to manage tasks independently in a fast-paced research environment.
Preferred Qualifications
Experience with time series modeling, forecasting, and anomaly detection.
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 in a PhD or Master’s program in Computer Science, Data Science, or related fields; or career transitioners/early career professionals.
Living within reasonable commuting distance from selected NASA site for the project selected: Langley Research Center, NASA Katherine Johnson IV&V Facility West Virginia, NASA Ames Research Center, Mountain View CA, or NASA HQ Washington, D.C.
Citizenship Requirements
U.S. citizenship or permanent residency required for in-person positions. Remote positions open to all U.S. authorized workers.
EEO Statement
AMA is an affirmative action/equal opportunity employer and does not discriminate against any applicant for employment or employee because of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, protected veteran status, or any other characteristic prohibited under federal, state, or local laws.
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