Dylan Engelbrecht

Senior Simulation Engineer | AI & Synthetic Data Specialist Senior Systems Engineer and Technical Author with expertise in Physics-based Simulation, Machine Learning integration, and High-Performance Computing within Unity. Published author with Springer Nature on ML-Agents (Reinforcement Learning) and Scalable Network Architectures. I engineer high-fidelity environments for Enterprise VR and large-scale applications. I bridge the gap between theoretical AI models and real-time 3D simulation, optimizing pipelines for performance and building custom tooling for automated workflows. Advisory & Consulting: Agent OperatorAvailable for strategic advisory roles focused on autonomous agent systems and simulation architecture.Design and implementation of Model-Based Reinforcement Learning within 3D environments.Architecture of intelligent agents, specifically neural network integration, reward function design, and hyperparameter tuning.Python (PyTorch/TensorFlow) training loops integrated with Unity C# execution environments.Technical guidance on Server-Authoritative architecture and data persistence for large-scale synchronous systems.Latency management and advanced transport layers for distributed simulation and digital twins. Technical PublicationsIntroduction to Unity ML-Agents: Neural Networks & Simulation Space (2023) Springer Nature (Apress) Authored the technical guide on Model-Based Reinforcement Learning within 3D environments. Detailed neural network integration and reward function design. Established best practices for bridging Python training loops with Unity C#.Building Multiplayer Games in Unity: Architecture & Scalability (2022) Springer Nature (Apress) Technical deep-dive into Server-Authoritative architecture and data persistence. Covered advanced transport layers, remote procedure calls (RPCs), and latency management for distributed simulation.