Frederick, MD)

Bascom Hunter Technologies•Baton Rouge, LA

21h•Hybrid

About The Position

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.

Requirements

Pursuing a Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Data Science, Artificial Intelligence, Machine Learning, Mathematics, or a related field.
Experience programming in Python.
Understanding of fundamental machine learning concepts, including model training, validation, testing, and performance evaluation.
Familiarity with at least one machine learning framework such as PyTorch, TensorFlow, or similar.
Strong analytical, mathematical, and problem-solving skills.
Ability to work with technical datasets and interpret experimental results.
Self-motivated with an interest in researching and applying emerging technologies.
Strong written and verbal communication skills.
Ability to work effectively as part of a multidisciplinary engineering team.

Nice To Haves

Experience developing or training custom machine learning models.
Experience with computer vision, signal processing, RF data, spectrograms, SAR imagery, or other sensor-based datasets.
Familiarity with model optimization, quantization, pruning, or edge AI deployment.
Experience with ONNX or other model interchange and deployment formats.
Familiarity with neuromorphic computing or spiking neural networks (SNNs).
Experience with Docker or containerized applications.
Familiarity with Kubernetes or scalable computing infrastructure.
Exposure to MLflow, DVC, MinIO, or similar MLOps and data management tools.
Experience developing APIs or microservices.
Familiarity with Go, Rust, C++, or another compiled programming language.
Experience with Git and collaborative software development workflows.
Familiarity with Linux development environments.
Previous internship, research, laboratory, project team, or other relevant AI/ML experience.

Responsibilities

Develop, train, evaluate, and improve custom machine learning models for a variety of aerospace, defense, and engineering applications.
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.
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.
Gain experience with the BrainChip Akida framework and explore approaches for adapting different machine learning use cases and data types to neuromorphic computing architectures.
Use industry-standard tools for experiment tracking, dataset management, model versioning, and ML workflows, including DVC, MinIO, MLflow, and similar technologies.
Develop inference microservices and transition experimental Python implementations toward scalable, high-performance applications using languages such as Go or Rust.
Apply testing and software engineering practices to improve the reliability and repeatability of AI/ML applications and inference services.
Build and deploy containerized applications using Docker and develop solutions capable of operating across scalable environments and workflows.
Gain exposure to technologies such as Kubernetes and other tools used to orchestrate containerized AI/ML workloads.
Explore machine learning applications for manufacturing defect detection, materials analysis, image classification, and other computer vision use cases.
Investigate machine learning approaches for image, RF, spectrogram, synthetic aperture radar (SAR), sensor, and other technical datasets.
Develop automation for monitoring model and data drift, ingesting new data, retraining models, evaluating candidate replacements, and comparing model performance.
Assist in developing repeatable data ingestion, transformation, training, evaluation, and deployment pipelines.
Evaluate methods for deploying machine learning models on specialized and resource-constrained computing architectures.
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.