CoRover AI Research Engineer Interview: Rounds, Questions and Preparation

What the job involves

AI Research Engineers in fast-growing AI and deeptech companies design, train, and evaluate machine learning models, often focusing on NLP, speech, or conversational AI. Day to day work includes reading research papers, running experiments, building data pipelines, and shipping models into products. At smaller firms like CoRover, you may also work on deployment, optimisation, and cross-functional collaboration.

Role and technical knowledge

1. 1

   What is the difference between a research engineer and a data scientist in an AI product company?

   Highlight that research engineers focus on model innovation, experimentation, and implementation, while data scientists often focus on analysis, metrics, and business insights. Mention overlap and collaboration.

2. 2

   Explain the attention mechanism in transformers and why it is useful for conversational AI.

   Cover query-key-value, self-attention, and how it captures long-range dependencies. Link to handling context in chatbots.

3. 3

   How would you evaluate a speech-to-text model for a multilingual Indian user base?

   Discuss word error rate (WER), language-specific challenges like code-switching, accents, and dataset diversity. Mention per-language evaluation.

4. 4

   What are some techniques to reduce model size for deployment on edge devices?

   Mention pruning, quantization, knowledge distillation, and using efficient architectures like MobileBERT or TinyBERT.

Practical scenarios and problem solving

1. 5

   You have a chatbot that performs well on English but poorly on Hinglish. How would you improve it?

   Suggest collecting code-mixed data, fine-tuning a multilingual model, using transliteration, and evaluating with Hinglish-specific metrics.

2. 6

   Your model's training loss decreases but validation loss increases. What do you do?

   Identify overfitting. Mention regularization, dropout, early stopping, data augmentation, and reducing model complexity.

3. 7

   How would you build a quick prototype for a FAQ chatbot for a new customer with limited data?

   Propose using a pre-trained language model, few-shot learning, or retrieval-based approach with embeddings. Mention iterative data collection.

4. 8

   Describe how you would deploy a trained NLP model to handle 100 requests per second.

   Cover model serving with FastAPI or TorchServe, batching, caching, load balancing, and monitoring latency.

Behavioural and teamwork

1. 9

   Tell me about a time you had to learn a new AI technique quickly for a project.

   Use STAR method. Emphasise self-learning, applying the technique, and the outcome. Show curiosity and adaptability.

2. 10

   Describe a situation where you disagreed with a teammate on a model approach. How did you resolve it?

   Show listening, data-driven discussion, and willingness to experiment. Highlight respect and focus on project goals.

3. 11

   How do you prioritise when you have multiple experiments to run and limited compute?

   Mention impact vs effort, starting with smallest viable experiments, using logging, and communicating trade-offs with the team.

HR and motivation

1. 12

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

   Show enthusiasm for ownership, rapid iteration, and direct impact. Mention interest in deeptech and learning across the stack.

2. 13

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

   Align with growing into a specialist or lead role, contributing to research and product. Avoid vague answers.

3. 14

   What recent AI research paper or breakthrough excited you, and why?

   Pick a relevant paper (e.g., LoRA, RAG, Whisper) and explain its impact simply. Show you follow the field.

4. 15

   Are you comfortable working in a startup environment with changing priorities?

   Give an example of adapting to change. Emphasise flexibility, communication, and focus on user impact.