A computer vision engineer builds systems that classify, detect, segment and read information from images and video, for uses like quality inspection, retail shelf analytics, document OCR and surveillance. Interviews test CNN and transformer fundamentals, detection and segmentation metrics, PyTorch and OpenCV coding, data labelling strategy and handling domain shift. Senior rounds add deploying optimised models to edge devices and GPUs at real-time speed.
CNNs and vision transformers Object detection and segmentation PyTorch and OpenCV Image data labelling and augmentation Model optimisation for edge inference
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01
Your image classifier trained on 2,000 images memorises the training set within 5 epochs. What do you try?
With so little data, I would use transfer learning from a pretrained model like ResNet or EfficientNet and fine-tune only the top layers first. I would add data augmentation such as flips, crops and colour changes, use dropout and weight decay, and apply early stopping on validation loss. Collecting more labelled images or using a smaller model also helps. I would check for duplicate images across train and validation sets too.
02
How do you choose a learning rate for a neural network?
Regularization adds a penalty on weights to reduce overfitting. L1 (Lasso) adds |w| and can push features to exactly zero. L2 (Ridge) adds w² and shrinks weights smoothly.
04
What is the difference between semantic, instance and panoptic segmentation?
Semantic segmentation gives each pixel a class, such as road or car, but does not separate two cars. Instance segmentation separates individual objects of countable classes, giving each car its own mask. Panoptic segmentation combines both: every pixel gets a class, and countable objects also get an instance ID, while background stuff like sky stays semantic. Autonomous driving and robotics often need panoptic or instance output.
05
A Pandas merge suddenly returns many more rows than the left table had. What happened?
The join key is probably not unique on the right side, so each left row matches several right rows and the result multiplies. Check with right['key'].duplicated().sum(). Other causes are null or blank keys matching each other after cleaning, or joining on the wrong column. Fix it by deduplicating or aggregating the right table first, and use merge(..., validate='many_to_one') so Pandas raises an error when the assumption breaks.
SVM finds the maximum-margin separating hyperplane. Kernels such as RBF compute similarity in a higher-dimensional space implicitly, so non-linear boundaries are possible.
07
What is mean Average Precision (mAP) in object detection?
For each class, detections are ranked by confidence and a precision-recall curve is computed, where a detection is a true positive if its IoU with an unmatched ground truth box passes a threshold. Average Precision is the area under that curve. mAP is the mean across classes. mAP@0.5 uses IoU 0.5, while mAP@0.5:0.95 averages over thresholds from 0.5 to 0.95 and rewards tighter boxes.
Mixed precision training uses 16-bit floats, float16 or bfloat16, for most operations while keeping a 32-bit master copy of the weights. It roughly halves memory use and speeds up training on modern GPUs with tensor cores. With float16, a loss scaler multiplies the loss to avoid gradients underflowing to zero. bfloat16 has a wider range and usually needs no scaling. Final accuracy is normally the same as full precision.
09
Why do residual connections make very deep networks easier to train?
A residual block adds its input to its output, so the layers learn a residual F(x) and the block outputs F(x) plus x. If extra layers are not useful, they can learn to output near zero and the block behaves like identity. The skip path also lets gradients flow directly to early layers, reducing vanishing gradients. This is why ResNets with 50 or 100-plus layers train well where plain deep networks degrade.
10
What does CI/CD mean for machine learning projects?
Continuous integration runs automatic checks on every code change: unit tests for feature code, data validation, linting and a quick training run on sample data. Continuous delivery packages the model and service, for example as a Docker image, and deploys it through staging to production after evaluation gates, such as the new model beating the current one on a holdout set. Some teams add continuous training, which retrains on new data automatically.
11
Tell me about a time you had to learn a new deep learning framework or tool quickly.
Situation: my internship project used PyTorch, but I had only used Keras. Task: build a text classifier within two weeks. Action: I followed the official tutorials for two days, rebuilt a small Keras model in PyTorch to compare, and asked a senior to review my training loop. Result: I delivered the model on time with 88 percent F1. I learned that rebuilding something familiar is the fastest way into a new tool.
12
You need to extract fields from scanned invoices that come in hundreds of different layouts. How would you design it?
Fixed templates will not scale to hundreds of layouts. Start with preprocessing: deskew, denoise and detect text regions. Run OCR to get words with their positions. Then use a layout-aware document model, or a vision-language model, to map words to fields like invoice number, GSTIN and total. Add validation rules, such as GSTIN format and line items summing to the total, and send low-confidence documents to human review.
13
You must classify support messages into 40 categories, but some categories have only 20 examples. What do you do?
Start with a pretrained sentence embedding model plus a simple classifier, or fine-tune a small transformer with class weights. For the rare categories, try few-shot approaches such as SetFit or an LLM with examples in the prompt, and generate or collect more examples. Merge categories that are rarely distinguishable. Report per-class F1 rather than overall accuracy, and route low-confidence predictions to a human queue.
14
Write PyTorch code to fine-tune a pretrained ResNet-18 for a 5-class problem by replacing its final layer.
import torch, torch.nn as nn
from torchvision import models
model = models.resnet18(weights=models.ResNet18_Weights.DEFAULT)
for p in model.parameters():
p.requires_grad = False # freeze backbone
model.fc = nn.Linear(model.fc.in_features, 5)
opt = torch.optim.AdamW(model.fc.parameters(), lr=1e-3)
loss_fn = nn.CrossEntropyLoss()
# train head first, then unfreeze later layers with a lower lr
15
You assign values to a filtered DataFrame and Pandas shows a SettingWithCopyWarning. What is going on and how do you fix it?
The warning appears when you assign values to something that may be a copy of a slice instead of the original DataFrame, for example df[df.age > 30]['flag'] = 1. The original may not change. Fix it by using a single .loc call, like df.loc[df.age > 30, 'flag'] = 1, or by taking an explicit .copy() when you really want a separate DataFrame. Copy-on-write in newer Pandas versions makes this behaviour more predictable.
16
Tell me about a time you deployed a model under a tight deadline.
Situation: a pricing model had to go live before a sale in ten days. Task: deploy safely in that window. Action: I cut scope to batch scoring instead of real-time, reused our standard Docker and Airflow template, wrote tests for the key features and set up a dashboard for score distribution. Result: it went live two days early and ran without incidents during the sale. Real-time scoring followed a month later.
17
What is the difference between SGD and Adam optimisers?
SGD updates weights using the gradient times a fixed learning rate, often with momentum. Adam keeps running averages of gradients and their squares, giving each parameter its own adaptive step size. Adam converges fast with little tuning and is the default for transformers. Well-tuned SGD with momentum sometimes generalises better on vision tasks. AdamW, which decouples weight decay, is the common choice for modern deep learning.
18
Your GPU model-serving bill has doubled while traffic grew only 20 percent. How do you bring cost down?
Profile utilisation first; idle or under-filled GPUs are common. Enable dynamic batching so each GPU call handles several requests, right-size instance types and autoscale on queue depth rather than CPU. Reduce model cost with quantisation, distillation or a smaller model for easy requests. Cache repeated predictions, move non-urgent work to batch jobs on cheaper hardware, and track cost per thousand predictions as a standing metric.
19
Your notebook runs on your laptop but fails on a colleague's machine. How do you make the work reproducible?
First pin the environment: a requirements.txt or conda environment file with exact versions, or a Docker image. Set random seeds for NumPy, scikit-learn and any framework you use. Remove hard-coded local paths and read them from a config file. Make sure data comes from a shared, versioned location. Finally, restart the kernel and run the notebook top to bottom, so hidden cell order bugs are caught before sharing.
A function using yield that produces values lazily, one at a time. It saves memory when processing large files or streams because the full sequence is never held in RAM.
A model registry is a central store for trained model versions along with their metadata: training data version, code commit, parameters, metrics and owner. Models move through stages such as staging, production and archived, with approvals. It gives one source of truth for which model is live, makes rollback to a previous version easy and supports audits. Tools like MLflow Model Registry are commonly used.
27
Tell me about a time you had to explain a deep learning model's mistake to a client or stakeholder.
Situation: our document classifier mislabelled several legal notices, and the client was upset. Task: explain clearly and restore confidence. Action: I showed the misclassified examples, explained that they used a format absent from training data, and avoided jargon. I proposed adding those samples, a confidence threshold for human review and weekly error reports. Result: errors on that format fell sharply and the client extended the project.
28
What is subword tokenisation like BPE or WordPiece?
Subword tokenisers split text into frequent pieces between characters and full words. Byte Pair Encoding starts from characters and repeatedly merges the most frequent pairs. WordPiece, used by BERT, is similar but chooses merges by likelihood. Rare words like unhappiness become un, happi, ness, so there are no unknown words and the vocabulary stays around 30,000 to 100,000. Token counts also decide LLM cost and context use.
Situation: on an insurance claims model, my first features gave average results. Task: improve fraud detection. Action: I sat with two claim investigators for a day and asked what made them suspicious. They mentioned claims filed soon after policy start and repeated garage names. I turned these into features and checked them with the team. Result: recall at the same precision rose by 12 points. Experts often know signals the data hides.
31
How do you use groupby with multiple aggregations?
Define churn and the prediction window → build usage and billing features → handle imbalance → train gradient boosting → evaluate with PR-AUC and lift → explain with SHAP → deploy as batch scoring or an API → monitor drift → hand top-risk users to the retention team.
33
Your deployed model gives different predictions than the same model in your notebook. Why?
Usually preprocessing differs between training and serving. Check that the same pipeline object was saved, not just the estimator, and that category order, missing value handling and scaling match. Compare library versions, feature types like strings versus integers, column order, and time zones in date features. Send the same few rows through both paths and compare features step by step to find the first mismatch.
During training it randomly switches off a fraction of neurons so the network cannot depend on any single unit, acting as a regulariser against overfitting.
A mutex in CPython that lets only one thread execute Python bytecode at a time. Use multiprocessing for CPU-bound work and threads or asyncio for I/O-bound work.
36
What is the difference between saving a model with pickle, joblib and ONNX?
pickle serialises Python objects; it is simple but tied to Python and library versions, and loading untrusted pickle files is a security risk. joblib is similar but more efficient for objects with large NumPy arrays, like scikit-learn models. ONNX is an open, language-neutral format that runs in optimised runtimes across languages and hardware, useful for fast serving. Always store library versions alongside pickle or joblib files.
Two-stage detectors, like Faster R-CNN, first propose candidate regions and then classify and refine each one, which gives strong accuracy, especially for small objects, but is slower. One-stage detectors, like YOLO and SSD, predict boxes and classes directly over a dense grid in a single pass, making them fast enough for real-time video. Modern one-stage models have closed most of the accuracy gap, so they dominate edge and video use cases.
39
Difference between a shallow copy and a deep copy?
A shallow copy duplicates the outer object but shares nested objects. A deep copy (copy.deepcopy) recursively duplicates everything, so changes to nested items do not leak.
40
Write a FastAPI endpoint that serves predictions from a saved scikit-learn model.
from fastapi import FastAPI
from pydantic import BaseModel
import joblib
import pandas as pd
app = FastAPI()
model = joblib.load('model.joblib')
class Customer(BaseModel):
tenure: int
monthly_spend: float
plan: str
@app.post('/predict')
def predict(c: Customer):
X = pd.DataFrame([c.model_dump()])
return {'churn_probability': float(model.predict_proba(X)[0, 1])}
Frequently asked questions
What does a Computer Vision Engineer do?
A computer vision engineer builds systems that classify, detect, segment and read information from images and video, for uses like quality inspection, retail shelf analytics, document OCR and surveillance. Interviews test CNN and transformer fundamentals, detection and segmentation metrics, PyTorch and OpenCV coding, data labelling strategy and handling domain shift. Senior rounds add deploying optimised models to edge devices and GPUs at real-time speed.
Which skills are tested in a Computer Vision Engineer interview?
Expect questions on CNNs and vision transformers, Object detection and segmentation, PyTorch and OpenCV, Image data labelling and augmentation, Model optimisation for edge inference. Most rounds also ask you to explain a project and solve one practical problem live.
How do I prepare for a Computer Vision Engineer interview?
Revise Computer Vision, Deep Learning and NLP, Python for Data Science, MLOps and Model Deployment first using the topic sheets, practise the questions on this page aloud, then do one full mock interview to practise timing.