This study builds on life course theory, focusing on predicting future life events (ages 56-60) based on past sequences (ages 18-55). Using the Transformer encoder-decoder framework, we treat life events as sequential data, similar to words in a sentence, to capture patterns and relationships over time. With only 11 social employment states and basic demographic information, we develop a simple yet generalizable model. Our transformer model achieves above 80% accuracy in predicting life transitions, particularly for individuals with stable life paths. It also identifies deviations from recent patterns, highlighting the impact of earlier life experiences on future outcomes.