How well can individuals’ parental background and previous life experiences predict their mid-life Socio-Economic Status (SES) attainment? This question is central to stratification research, as a strong power of earlier experiences in predicting later-life outcomes signals substantial intra- or intergenerational status persistence, or put simply, social rigidity. Running machine learning models on panel data to predict outcomes that include hourly wage, total income, family income, and occupational status, we find that a large number (around 4,000) of predictors commonly used in the stratification literature improves the prediction of one’s life chances in middle to late adulthood by about 10 to 50 percent, compared with a null model that uses a simple mean of the outcome variable. The level of predictability depends on the specific outcome being analyzed, with labor market indicators like wages and occupational prestige being more predictable than broader socioeconomic measures such as overall personal and family income. Grouping a comprehensive list of predictors into four unique sets that cover family background, childhood and adolescence development, early labor market experiences, and early adulthood family formation, we find that including income, employment status, and occupational characteristics at early career significantly improves models’ prediction accuracy for mid-life SES attainment. Further, we illustrate the application of predictive models to examine sources of between-group disparity by life stages. Using the Black-white difference as the example, we find that racial differences in early labor market experiences are the most critical in explaining racial inequality in mid-life SES attainment, especially for low-income individuals.