Tiny machine learning has enabled resource-constrained end devices to make intelligent inferences for time-critical and remote applications from unstructured data. However, realizing edge artificial intelligence systems that can perform long-term high-level reasoning and obey the underlying physics within the tight platform resource budget is challenging. The talk introduces the concept of neurosymbolic auto tiny machine learning, where the synergy of physics-based process models and neural operators is automatically co-optimized based on platform resource constraints. Neurosymbolic artificial intelligence combines the context awareness and integrity of symbolic techniques with the robustness and performance of machine learning models. The talk showcases how fast, gradient-free and black-box Bayesian optimization can automatically construct the most performant learning-enabled, physics, and context-aware intelligent programs from a search space containing neural and symbolic operators. Several previously unseen applications are showcased, including onboard physics-aware neural-inertial navigation, on-device human activity recognition, on-chip fall detection, neural-Kalman filtering, and co-optimization of neural and symbolic processes.