AI​/ML Engineering Intern Baton Rouge, LA​/Frederick, MD

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Intern Baton Rouge, Baton Rouge, LA, US

Bascom Hunter is seeking an AI/ML Engineering Intern to support the development, optimization, deployment, and productization of machine learning models for aerospace and defense applications. The intern will gain hands‑on experience developing custom models, optimizing models for edge deployment, building scalable inference solutions, and developing the infrastructure needed to move machine learning capabilities from experimentation toward deployable systems.

This position provides exposure to the full machine learning lifecycle, including data preparation, model development and training, optimization, deployment, MLOps, inference, and performance evaluation.

Bascom Hunter provides custom solutions to the Department of Defense and aerospace markets, with capabilities spanning advanced electronics, communications, RF systems, environmental control, thermal management, power, and other mission‑critical technologies.

• Custom Model Development:
  Develop, train, evaluate, and improve custom machine learning models for a variety of aerospace, defense, and engineering applications.
• Model Optimization and Pruning:
  Evaluate and apply model pruning and other optimization techniques to reduce model size and computational requirements for deployment on resource‑constrained and edge computing hardware.
• Model Generalization and Productization:
  Convert and deploy models using formats and frameworks such as ONNX and explore how trained models can be incorporated into applications outside of Python environments.
• Neuromorphic Computing:
  Gain experience with the Brain Chip Akida framework and explore approaches for adapting different machine learning use cases and data types to neuromorphic computing architectures.
• AI/ML Tooling:
  Use industry‑standard tools for experiment tracking, dataset management, model versioning, and ML workflows, including DVC, MinIO, MLflow, and similar technologies.
• Inference and Microservices:
  Develop inference microservices and transition experimental Python implementations toward scalable, high‑performance applications using languages such as Go or Rust.
• Test‑Driven Development:
  Apply testing and software engineering practices to improve the reliability and repeatability of AI/ML applications and inference services.
• Containers and Infrastructure:
  Build and deploy containerized applications using Docker and develop solutions capable of operating across scalable environments and workflows.
• Scalable Deployment:
  Gain exposure to technologies such as Kubernetes and other tools used to orchestrate containerized AI/ML workloads.
• Computer Vision and Defect Detection:
  Explore machine learning applications for manufacturing defect detection, materials analysis, image classification, and other computer vision use cases.
• Multi‑Domain Data Analysis:
  Investigate machine learning approaches for image, RF, spectrogram, synthetic aperture radar (SAR), sensor, and other technical datasets.
• AI/MLOps:
  Develop automation for monitoring model and data drift, ingesting new data, retraining models, evaluating candidate replacements, and comparing model performance.
• Edge AI:
  Evaluate methods for deploying machine learning models on specialized and resource‑constrained computing architectures.
• Research and Experimentation:
  Research emerging AI/ML technologies, architectures, tools, and techniques and evaluate their applicability to aerospace and defense applications.
• Document model development, experiments, performance results, deployment processes, and technical findings.

• Pursuing a…