Cerebras Systems India Applied Scientist Interview: Rounds, Questions and Preparation

What the job involves

Applied Scientists in AI and deeptech build and deploy machine learning models that solve real-world problems, often working at the intersection of research and engineering. At companies like Cerebras Systems India, they might work on optimizing models for wafer-scale compute, developing novel architectures, or improving training efficiency. The role involves coding, experimentation, and collaboration with hardware and software teams.

Role and Technical Knowledge

1. 1

   Explain the difference between a wafer-scale engine and a GPU cluster for training deep learning models.

   Discuss memory bandwidth, interconnect, and scalability. Mention how wafer-scale reduces communication overhead and enables larger models.

2. 2

   How would you optimize a transformer model for inference on a resource-constrained edge device?

   Cover quantization, pruning, knowledge distillation, and efficient attention mechanisms. Mention trade-offs between accuracy and latency.

3. 3

   What are the challenges in distributed training of large language models, and how can they be addressed?

   Talk about data parallelism, model parallelism, gradient compression, and communication bottlenecks. Mention techniques like ZeRO and pipeline parallelism.

4. 4

   Describe a recent advancement in deep learning that you find impactful and explain why.

   Choose a relevant paper (e.g., efficient transformers, diffusion models). Summarize the key idea, its significance, and potential applications.

Practical Scenarios

1. 5

   You are given a dataset with imbalanced classes. How would you approach training a model for high recall?

   Discuss resampling, class weighting, and appropriate metrics. Mention techniques like focal loss and evaluation using precision-recall curves.

2. 6

   Your model's training loss is decreasing but validation loss is increasing. What steps would you take?

   Identify overfitting. Suggest regularization, dropout, early stopping, and data augmentation. Also check for data leakage.

3. 7

   How would you deploy a machine learning model to production in a company like Cerebras?

   Cover model serialization, API creation, monitoring, and scaling. Mention CI/CD, versioning, and A/B testing.

4. 8

   You need to reduce the inference time of a BERT model by 50% with minimal accuracy drop. What techniques would you use?

   Suggest quantization, pruning, layer dropping, and using distilled versions like DistilBERT. Discuss benchmarking and validation.

Behavioural and HR

1. 9

   Why do you want to work as an Applied Scientist in the AI and deeptech sector, and specifically at a company like Cerebras?

   Show passion for AI and hardware-software co-design. Mention Cerebras's unique wafer-scale technology and how it aligns with your interests.

2. 10

   Describe a time when you had to learn a new technology quickly for a project.

   Use STAR method. Highlight self-learning, resourcefulness, and application of the new skill to achieve a result.

3. 11

   Tell me about a project where you faced a significant technical challenge. How did you overcome it?

   Focus on problem-solving, persistence, and collaboration. Explain the challenge, your approach, and the outcome.

4. 12

   Where do you see yourself in five years?

   Align your growth with the company's mission. Mention deepening expertise in AI and taking on leadership roles in research or product development.

Coding and Algorithms

1. 13

   Implement a function to compute the softmax of a vector in Python, ensuring numerical stability.

   Subtract the max value before exponentiation. Handle edge cases like empty input or infinities.

2. 14

   Given a list of numbers, find the longest increasing subsequence.

   Use dynamic programming or binary search for O(n log n). Explain the approach and complexity.

3. 15

   Write a function to perform matrix multiplication using NumPy and then optimize it using broadcasting.

   Show understanding of vectorization and efficiency. Discuss time complexity and memory usage.

4. 16

   How would you implement a simple neural network from scratch using NumPy?

   Cover forward propagation, backpropagation, and gradient descent. Mention activation functions and loss calculation.