Sigmoid Computer Vision Engineer Interview: Rounds, Questions and Preparation

What the job involves

Computer Vision Engineers at AI and deeptech companies like Sigmoid build and deploy deep learning models for image and video understanding, such as object detection, segmentation, and OCR. Day to day, they work with large datasets, train and optimize models on GPUs, and integrate them into production pipelines for clients in retail, healthcare, or manufacturing. Freshers typically start by implementing models from papers, debugging training issues, and learning to balance accuracy with latency and cost.

Role and Technical Knowledge

1. 1

   Explain how a convolutional layer works and why it is suited for image data.

   Cover local receptive fields, weight sharing, and translation invariance. Mention how filters learn hierarchical features from edges to objects.

2. 2

   What is the difference between object detection and semantic segmentation? Give a use case for each in a deeptech company.

   Detection outputs bounding boxes and class labels; segmentation assigns a class to each pixel. Use cases: detection for retail shelf monitoring, segmentation for medical image analysis.

3. 3

   How would you handle a dataset with very few labeled images for a custom vision task?

   Discuss transfer learning, data augmentation, semi-supervised learning, and synthetic data generation. Mention starting with a pretrained backbone and fine-tuning.

4. 4

   What metrics would you use to evaluate an object detection model, and why is mAP preferred over simple accuracy?

   Explain precision, recall, IoU, and mAP. mAP handles multiple classes and localization quality, unlike accuracy which ignores box overlap.

Practical Scenarios and Problem Solving

1. 5

   You need to deploy a real-time vehicle detection model on an edge device with limited compute. How would you approach it?

   Suggest lightweight architectures like MobileNet or YOLO-tiny, quantization, pruning, and using TensorRT or ONNX Runtime. Mention trade-offs between speed and accuracy.

2. 6

   Your model performs well on validation but poorly on client images taken in different lighting. What do you do?

   Analyze domain shift, apply color normalization, histogram equalization, or augment training data with varied lighting. Consider fine-tuning on a small set of client-like images.

3. 7

   How would you build a pipeline to detect and read text from product labels in a warehouse?

   Outline steps: image capture, preprocessing, text detection (e.g., EAST or CRAFT), OCR (Tesseract or CRNN), and post-processing. Mention handling rotation and blur.

4. 8

   You have a large unlabeled video dataset and a small labeled set. How can you use both to improve a vision model?

   Discuss self-supervised pretraining (e.g., SimCLR, MoCo), pseudo-labeling, and consistency regularization. Emphasize leveraging unlabeled data for representation learning.

Coding and Implementation

1. 9

   Write a function to compute Intersection over Union (IoU) between two bounding boxes.

   Show correct calculation of intersection area, union area, and handling of no overlap. Use clear variable names and edge cases.

2. 10

   Implement a 2D convolution operation in NumPy for a single-channel image and a 3x3 filter.

   Demonstrate padding, stride, and sliding window logic. Mention that real frameworks use optimized libraries.

3. 11

   Given a list of predicted boxes and ground truth boxes, write code to calculate precision and recall at a fixed IoU threshold.

   Sort predictions by confidence, match to ground truth, and count true positives, false positives, and false negatives. Show the formulas.

4. 12

   How would you load and preprocess a batch of images for training in PyTorch? Write a short code snippet.

   Use torchvision transforms, DataLoader, and normalization. Mention augmentations like random flip and crop.

Behavioural and HR Questions

1. 13

   Tell me about a computer vision project you built. What was your role and what challenges did you face?

   Use STAR format. Highlight technical decisions, dataset issues, and how you measured success. Show ownership and learning.

2. 14

   Describe a time you had to learn a new tool or concept quickly for a project.

   Give a specific example, like picking up a new framework or paper. Explain your learning process and the outcome.

3. 15

   Why do you want to work as a Computer Vision Engineer in a deeptech company like Sigmoid?

   Connect your interest in solving real-world problems with images to the company's focus on AI solutions. Mention eagerness to work on production-scale vision systems.

4. 16

   How do you handle feedback on your model or code that you disagree with?

   Show openness, data-driven discussion, and willingness to test alternatives. Emphasize collaboration and objective evaluation.