Sigmoid Data Solutions AI Research Engineer Interview: Rounds, Questions and Preparation

What the job involves

AI Research Engineers at fast-growing AI and deeptech companies design, train, and evaluate machine learning models, often focusing on deep learning, NLP, or computer vision. Day to day, they read research papers, implement experiments in PyTorch or TensorFlow, run ablation studies, and work with data engineers to productionize models. They also collaborate with product teams to translate business problems into measurable ML objectives.

Role and Technical Knowledge

1. 1

   Explain the difference between a research engineer and a data scientist in an AI deeptech company.

   A strong answer highlights that research engineers focus more on model architecture, experimentation, and implementation, while data scientists often focus on analysis, metrics, and business insights. Mention collaboration between the two.

2. 2

   How would you handle a dataset with severe class imbalance for a fraud detection model?

   Cover techniques like resampling (SMOTE, undersampling), class weights, using appropriate metrics (precision-recall AUC, F1), and possibly anomaly detection methods. Emphasize validation strategy.

3. 3

   What is the attention mechanism in Transformers, and why is it useful for NLP tasks?

   Explain scaled dot-product attention, how it captures long-range dependencies, and its role in parallelization compared to RNNs. Mention multi-head attention and positional encodings.

4. 4

   Describe how you would evaluate a named entity recognition (NER) model beyond accuracy.

   Discuss entity-level precision, recall, F1 score, and confusion between entity types. Mention per-class metrics and error analysis on boundary cases.

Practical Scenarios and Implementation

1. 5

   You have a trained image classification model that performs well on validation but poorly on a new client's data. How do you debug and improve it?

   Consider domain shift, data drift, and overfitting. Suggest fine-tuning on client data, data augmentation, and checking for label noise. Mention monitoring and retraining pipelines.

2. 6

   Write pseudocode to compute the cosine similarity between two vectors and explain its use in recommendation systems.

   Show dot product divided by product of norms. Explain that it measures orientation, useful for embeddings in recommendations. Mention normalization and handling zero vectors.

3. 7

   How would you reduce inference latency of a BERT-based model for a real-time application?

   Mention model distillation, quantization, pruning, using ONNX or TensorRT, and caching. Also consider smaller architectures like DistilBERT or ALBERT.

4. 8

   Given a large text corpus, how would you build a topic modeling pipeline?

   Outline preprocessing (tokenization, stopword removal), then LDA or BERTopic. Discuss evaluation with coherence scores and visualization with pyLDAvis.

Behavioural and HR Questions

1. 9

   Why do you want to work as an AI Research Engineer at a fast-growing deeptech company rather than a large tech firm?

   Show enthusiasm for hands-on research, rapid iteration, and broad impact. Mention eagerness to learn across the stack and contribute to cutting-edge projects.

2. 10

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

   Use STAR method: situation (e.g., hackathon or coursework), task, action (how you learned), result (successful implementation). Highlight self-driven learning.

3. 11

   How do you handle disagreement with a senior researcher about a model approach?

   Emphasize data-driven discussion, respectful communication, and willingness to prototype and compare. Show ability to accept feedback and collaborate.

4. 12

   Where do you see yourself in three years in the AI research field?

   Align with company growth: leading a small research project, publishing, or specializing in a domain like NLP or computer vision. Show commitment to continuous learning.

Coding and Mathematics

1. 13

   Implement a function to compute the softmax of a vector in Python without using libraries.

   Handle numerical stability by subtracting the max before exponentiation. Show vectorized NumPy implementation and explain each step.

2. 14

   Given a list of numbers, find the pair with the maximum product. Write an efficient algorithm.

   Sort and consider product of two largest or two smallest (if negatives). O(n log n) or O(n) by tracking max1, max2, min1, min2. Explain edge cases.

3. 15

   Explain gradient descent and its variants (SGD, Adam). When would you use each?

   Cover batch, stochastic, and mini-batch. Adam combines momentum and RMSProp. Use SGD for fine-tuning, Adam for faster convergence on sparse gradients.

4. 16

   What is the curse of dimensionality, and how does it affect machine learning models?

   As dimensions increase, data becomes sparse, distance metrics lose meaning, and models need more data. Mention dimensionality reduction (PCA, t-SNE) and feature selection.