Scale AI Interview Questions

Sreenisha S. - "Let me tell you about a time where a website I managed suddenly showed slow performance and the mistake on our side was it was unnoticed until a user reported the issue to management. As a PM for that project, I took full responsibility of the situation and worked with the engineering team to quickly resolve it. This mistake taught me the importance of focusing and monitoring non functional requirements as well in addition to new feature development /adoption where I was mostly spending my time"See full answer

Himanshu P. - "def findfreetime(schedules): Step 1: Flatten the list of schedules into a single list of intervals all_intervals = [interval for schedule in schedules for interval in schedule] Handle edge case of an empty schedule if not all_intervals: return [] Step 2: Sort all intervals by their start time all_intervals.sort(key=lambda x: x[0]) Step 3: Merge overlapping intervals mergedbusy = [allintervals[0]] for currentstart, currentend in"See full answer

Rui B. - "Clarification and Assumption In house labeling vs outsourced? labeling text, images, bounding boxes, segmentations etc? what kind of issues have we observed? Assuming that data collection and pre/post-processing is done by different team and labeling starts with receiving data and ends with sending files back to my team. "outsourced, images containing objects, 2 dozen classes of objects to label bounding boxes." 2.Goal Quality is important because downstream effect. garbage in garbage out."See full answer

Amparo L. - "The most impressive thing that I’ve done was I got to do a fowards flip on the gym mat and I’ve gotten way flexible "See full answer

Shibin P. - "A typical computer vision pipeline consists of several key stages that process and analyze visual data to extract meaningful information. Here’s a general outline of the steps involved: Image Acquisition:Capturing images or videos using cameras or other imaging devices. Preprocessing steps such as resizing, cropping, and converting color spaces. Image Preprocessing:Noise reduction (e.g., using filters like Gaussian blur). Image normalization to standardize pixel values. Contrast e"See full answer

Xuelong A. - "I've participated in several competitions in Kaggle concerning medical images. My most recent competition deals with images of skin lesions and classifying them as either melanoma or not. I focused on fine-tuning pretrained models and ensembling them. I also like to keep track of the latest trends of computer vision research, with a focus on making models memory-efficient through model compression and interpretability."See full answer