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Studies show that young autistic adults are under- or unemployed, with almost half never holding a paying job in their 20’s. Unemployment within this population leads to decreased personal growth and increased dependence on caregivers. Research suggests that the interview process is one of the largest barriers to employment for this population. Autistic individuals often struggle with emotion regulation, which can be exacerbated by the interview process. To address this, we propose the use of a stress detection model in conjunction with a virtual reality interview simulator. This combination will allow for the interview to adapt to the state of the participant to improve the skills and engagement of the user and positively influence their comfort level. Data regarding negative affective responses to categories of questions can also be used to inform employers on better interviewing techniques. A model was designed using data obtained from neurotypical participants completing a modified Computerized Paced Serial Addition Task (PASAT-C) and evaluated on a dataset obtained from Autistic participants who took part in a simulated interview. Agreement between the model and ground truth was compared based on Pearson correlation coefficients. It was found that was r(289) = 0.28, which was statistically significant (p < .001; CI: 0.17 to 0.38). Our preliminary results provide evidence for the validity of observer-based labeling of data captured using a wrist-worn physiological sensor.