Modeling & Simulation AI/ML Engineer

Torch Technologies, Inc.•Huntsville, AL

9d•Onsite

About The Position

The Modeling & Simulation (M&S) AI/ML Engineer supports testing of complex systems within distributed Hardware-in-the-Loop (HWIL) and cloud-based simulation environments for the Missile Defense Agency (MDA). This role focuses on designing, developing, and deploying advanced AI and machine learning models to analyze simulation data, enhance predictive capabilities, and improve decision-making in support of mission-critical software development and test events. As a Modeling and Simulation Artificial Intelligence (AI)/ Machine Learning (ML) Engineer, your duties will include the following but not limited to:

Requirements

U.S. Citizenship
Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or related field (equivalent professional experience considered)
2+ years of related experience.
Active SECRET security clearance with ability to obtain and maintain TS/SCI
Software development experience
Strong Python development skills
Experience working with large datasets and/or streaming data
Proficiency with AI/ML and deep learning frameworks such as PyTorch, TensorFlow, Keras, Hugging Face, or similar
Understanding of machine learning concepts and model architectures (e.g., Decision Trees, Random Forests, LSTM/sequence models)
Experience implementing, training, evaluating, and optimizing ML models
Knowledge of data structures, algorithms, and performance optimization
Experience with Git and collaborative development workflows

Nice To Haves

Experience with Retrieval-Augmented Generation (RAG)
Experience with Model Context Protocols (MCP) or similar agent/tool interaction frameworks
Experience with GPU acceleration and CUDA architecture (drivers, runtime, APIs)
Experience with deep learning and reinforcement learning libraries
Experience building or supporting real-time data pipelines
Experience with data visualization and exploratory analysis tools (Matplotlib, Seaborn, Plotly, etc.)
Familiarity with model deployment, inference optimization, and MLOps practices
Experience developing and deploying models in cloud environments (AWS, Azure, or GCP)
Software testing experience, including development of test plans, procedures, and reports
Experience working in containerized or distributed environments
Experience with Linux development environments
Experience with Docker and containerized workflows
Familiarity with large-scale or streaming data processing frameworks

Responsibilities

Design, develop, and train machine learning and deep learning models (e.g., CNNs, RNNs, sequence models)
Process, cleanse, and validate large and complex datasets for model training and analysis
Perform statistical analysis and model tuning to optimize accuracy and performance
Deploy models into production environments and develop APIs for system integration
Automate and manage AI/ML data pipelines and infrastructure (MLOps practices)
Support real-time or near-real-time analytics within simulation and test environments
Collaborate with cross-functional engineering teams to define requirements and deliver AI-driven solutions
Stay current with emerging AI technologies, including Large Language Models (LLMs) and generative AI techniques
Support distributed simulation events, including occasional off-hours test activities