Together AI India NLP Engineer Interview: Rounds, Questions and Preparation

What the job involves

As an NLP Engineer at a fast-growing AI deeptech company, you will build, fine-tune, and deploy large language models and other NLP systems for real-world applications like chatbots, summarization, and search. Your day-to-day work involves data preprocessing, model training, evaluation, and optimizing inference for production. You will collaborate with ML engineers and product teams to ship language features that scale.

Role and Technical Knowledge

1. 1

   Explain the self-attention mechanism in transformers and why it is effective for NLP tasks.

   Cover how queries, keys, and values compute attention scores, and mention parallelization and long-range dependency handling.

2. 2

   What are the key differences between BERT and GPT architectures, and when would you use each?

   Discuss bidirectional vs autoregressive training, masked language modeling vs causal language modeling, and use cases like classification vs generation.

3. 3

   How do you handle out-of-vocabulary words or subword tokenization in modern NLP models?

   Explain Byte-Pair Encoding (BPE), WordPiece, or SentencePiece, and how they mitigate OOV issues.

4. 4

   Describe the process of fine-tuning a pre-trained language model for a specific downstream task.

   Mention task-specific data preparation, choice of learning rate, freezing layers, and evaluation on validation set.

Practical Scenarios and Problem Solving

1. 5

   You need to build a text summarization system for news articles. Outline your approach from data collection to deployment.

   Cover dataset curation, choosing a pre-trained model like BART or T5, fine-tuning, evaluation with ROUGE, and serving via API.

2. 6

   Your model's inference latency is too high for a real-time chatbot. How would you optimize it?

   Suggest model quantization, pruning, distillation, using ONNX runtime, or caching frequent responses.

3. 7

   How would you detect and mitigate bias in an NLP model used for resume screening?

   Discuss bias metrics, data augmentation, adversarial debiasing, and regular audits.

4. 8

   Given a low-resource language dataset, how would you build a functional NLP model?

   Mention transfer learning, multilingual models like mBERT or XLM-R, data augmentation, and active learning.

Coding and Implementation

1. 9

   Write a Python function to compute the attention scores from query and key matrices.

   Show matrix multiplication, scaling by sqrt(d_k), and softmax application.

2. 10

   Implement a simple tokenizer that splits text into words and handles punctuation.

   Use regex or basic string operations, and discuss handling of contractions and special characters.

3. 11

   Given a list of sentences, write code to generate embeddings using a pre-trained model from Hugging Face.

   Demonstrate loading a model like Sentence-BERT, tokenizing, and extracting pooled embeddings.

4. 12

   How would you debug a model that is overfitting on a small NLP dataset?

   Suggest regularization, dropout, data augmentation, early stopping, and cross-validation.

Behavioural and HR

1. 13

   Why do you want to work at a fast-growing AI deeptech company like Together AI India rather than a large tech firm?

   Show enthusiasm for cutting-edge research, ownership, and impact in a startup environment.

2. 14

   Describe a time you had to learn a new NLP technique quickly for a project.

   Use STAR method: situation, task, action (e.g., reading papers, online courses), and result.

3. 15

   How do you prioritize tasks when working on multiple NLP experiments with tight deadlines?

   Mention impact-based prioritization, timeboxing, and communication with stakeholders.

4. 16

   Tell me about a failure in a machine learning project and what you learned.

   Focus on a specific technical challenge, how you diagnosed it, and the lesson applied later.