Space AI ML Engineer Intern

The Aerospace Corporation; Colorado Springs (CO); On-site; USD 44,000 - 52,000; Full time

Benefits offered by this job

Competitive compensation

Job summary

The Aerospace Corporation is seeking Machine Learning Engineering Interns for the Data Science and Artificial Intelligence Department (DSAID) within the Information Systems and Cyber Division (ISCD). The selected candidates will work full-time onsite at our facilities in El Segundo, CA, Chantilly, VA or Colorado Springs, CO, contributing to AI/ML projects across space programs.

You will develop ML experiments, evaluate models for scalable systems, and present results to stakeholders.

Qualifications

• Pursuing a Master's or PhD in Computer Science, Computer Engineering, or related field.
• Available to work full-time for at least 10 weeks and return to study afterward.
• Minimum GPA 3.0; Bachelor's degree completed by start date.

Responsibilities

• Develop machine learning and data science experiments (NLFor NLP, CV, time-series, RL).
• Evaluate tech and data models for scalable, mission-critical apps.
• Collaborate with cross-functional teams to deliver features.
• Present results to customer stakeholders in written and verbal form.
• Foster a learning-focused team environment.

Skills

Python proficiency

PyTorch

Docker

Kubernetes

Unix/Linux

ML/AI concepts

MLOps knowledge

NVIDIA tools

Slurm

Cloud native

Education

Master's or PhD in Computer Science/related

Bachelor's degree completed by internship start

Tools

Docker

Kubernetes

PyTorch

MLFlow

Data Version Control

Slurm

Job description

The Aerospace Corporation is seeking Machine Learning Engineering Interns for the Data Science and Artificial Intelligence Department (DSAID) within the Information Systems and Cyber Division (ISCD). The selected candidates will work full-time onsite at our facilities in El Segundo, CA, Chantilly, VA or Colorado Springs, CO, contributing to AI/ML projects across space programs.

You will develop ML experiments, evaluate models for scalable systems, and present results to stakeholders.