Yellow.ai Labs AI Research Engineer Interview: Rounds, Questions and Preparation

What the job involves

AI Research Engineers in deeptech companies design, implement, and evaluate machine learning models, often focusing on natural language processing, conversational AI, or reinforcement learning. Day to day, they read research papers, prototype novel architectures, run experiments, and collaborate with product teams to turn research into scalable features. At established employers like Yellow.ai Labs, the work may involve improving intent detection, dialogue management, or multilingual language models for enterprise chatbots.

Role and technical knowledge

1. 1

   Explain the self-attention mechanism in Transformers and why it is useful for conversational AI.

   Cover how queries, keys, and values compute weighted sums, and highlight parallelization and long-range dependency capture compared to RNNs.

2. 2

   How would you handle out-of-vocabulary words in a multilingual intent classification model?

   Discuss subword tokenization (BPE, WordPiece), character-level embeddings, and using multilingual pretrained models like mBERT or XLM-R.

3. 3

   What is the difference between policy gradient and Q-learning in reinforcement learning for dialogue systems?

   Contrast on-policy vs off-policy learning, sample efficiency, and stability, and mention use cases like dialogue policy optimization.

4. 4

   Describe how you would evaluate a text summarization model for customer support tickets.

   Mention ROUGE scores, human evaluation for fluency and relevance, and domain-specific metrics like factuality or coverage of key issues.

Practical scenarios

1. 5

   You have a dataset of 10,000 customer queries but only 500 are labeled. How would you build a robust intent classifier?

   Propose semi-supervised learning, data augmentation, active learning, or few-shot learning with pretrained language models.

2. 6

   Your chatbot's responses are fluent but often irrelevant. How would you debug and improve the dialogue model?

   Suggest analyzing retrieval vs generation, adding relevance scoring, using reinforcement learning from human feedback, and checking context handling.

3. 7

   You need to deploy a large language model on a low-latency production system. What optimizations would you consider?

   Discuss quantization, pruning, knowledge distillation, ONNX runtime, and caching or batching strategies.

4. 8

   How would you detect and mitigate bias in a sentiment analysis model for Indian languages?

   Mention bias audits across dialects, balanced datasets, fairness metrics, and adversarial debiasing techniques.

Research and problem-solving

1. 9

   Pick a recent NLP paper you have read. Summarize its key contribution and how you would implement it.

   Show clear understanding of the method, its novelty, and practical steps like reproducing results or adapting to a new dataset.

2. 10

   How would you design an experiment to compare two dialogue state tracking models?

   Define baselines, metrics (joint goal accuracy), datasets (MultiWOZ), and controls for fair comparison including hyperparameter tuning.

3. 11

   What are the limitations of current evaluation metrics for conversational AI, and how can we improve them?

   Discuss lack of semantic nuance in BLEU/ROUGE, need for human-in-the-loop evaluation, and task-specific metrics like task completion rate.

4. 12

   If your model's performance drops on a new domain, what steps would you take to adapt it?

   Cover domain adaptation techniques: fine-tuning, adversarial training, data augmentation, and few-shot learning with prompt tuning.

Behavioural and HR

1. 13

   Why do you want to work in AI research at a deeptech company rather than a general software role?

   Show passion for research, solving open-ended problems, and applying cutting-edge models to real products; mention specific areas like NLP or RL.

2. 14

   Describe a time you had to learn a new ML technique quickly for a project. How did you approach it?

   Use STAR method: situation, task, action (e.g., reading papers, online courses, implementing a prototype), and result (successful integration).

3. 15

   Tell me about a project where you had to collaborate with non-technical stakeholders. How did you ensure alignment?

   Highlight communication of technical trade-offs in simple terms, iterative feedback, and focusing on business impact.

4. 16

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

   Align with company growth: deepening expertise, publishing or patenting, and leading small research initiatives while contributing to product.