Apply machine learning algorithms to large sets of structured and unstructured data to solve a broad range of problems that include applications in autonomy, pattern recognition, target detection and tracking, recommendation and decision support systems, and robotic systems. Design, develop, document, test, debug, and deploy state-of-the-art AI/ML applications. Collaborate with cross-functional team to deploy AI/ML solutions in development and production environments. Bachelor's degree in Computer Science or related STEM degree with 5 years' experience OR Master's degree in Computer Science or related STEM degree with 3 years' experience Experience with one of more AI models and algorithms:

LLMs, DNN, genetic algorithms, embedding models, deep learning, etc. Experience developing systems with AI/ML frameworks and libraries such as PyTorch, TensorFlow, Keras, and Scikit-learn. Development experience should span the ML pipeline and include: working with novel datasets, data processing and preparation, training and fine-tuning ML systems, modifying existing deep learning architectures as well as coding from architecture diagrams, and evaluating system performance using established and novel metrics and methods. Ability to carry out rapid prototyping and proof-of-concept demonstrations. Possess general familiarity with applied machine learning concepts including: multilayer perceptrons, CNNs, LSTMs, transformer models, supervised/unsupervised learning methods, reinforcement learning methods, as well as statistical modeling and graphical models. Possess general familiarity with computational and statistical learning theory. Experience with web application development frameworks such as Flask, FastAPI. Understanding of software engineering best practices including source control and software architecture Able to obtain a U.S. Government Top Secret security clearance (U.S. citizenship is a pre-requisite) Experience with AWS or other cloud computing services. Experience with microservice architectures and related software methods including containerization and container orchestration. Familiarity with explainable AI, adversarial AI, active learning, and third wave approaches to AI. Familiarity with DevOps/DevSecOps/MLOps concepts. Familiarity with Linux-based operating systems. Demonstrated ability to work independently. Experience in working with LLMs (e.g. prompt engineering, LLM development frameworks, fine tuning, quantization) Experience in modeling and simulation. Experience with vector and/or graph databases