Graphcore India Machine Learning Engineer Interview: Rounds, Questions and Preparation

What the job involves

Machine Learning Engineers in AI and deeptech build and optimize ML models that run on specialized hardware, often for edge or data center deployments. Day to day, they work on model training, inference optimization, and integration with hardware accelerators, collaborating with hardware and software teams. At fast-growing companies like Graphcore India, they focus on making deep learning models efficient on novel processor architectures.

Role and Technical Knowledge

1. 1

   Explain how you would optimize a deep learning model for inference on a hardware accelerator like an IPU or TPU.

   Discuss quantization, pruning, and graph compilation; mention trade-offs between accuracy and speed, and hardware-specific optimizations like memory layout.

2. 2

   What are the key differences between training on GPUs versus specialized AI accelerators?

   Cover architecture differences (SIMD vs. MIMD), memory bandwidth, parallelism strategies, and how frameworks like TensorFlow and PyTorch handle device placement.

3. 3

   Describe the transformer architecture and why it is important for modern NLP models.

   Explain self-attention, multi-head attention, positional encoding, and its advantages over RNNs in handling long-range dependencies and parallelization.

4. 4

   How would you handle a dataset with severe class imbalance for a classification task?

   Mention techniques like resampling, SMOTE, class weighting, and evaluation metrics like F1-score or AUC-ROC instead of accuracy.

Practical Scenarios and Problem Solving

1. 5

   You need to deploy a model on an edge device with limited memory. Walk through your approach.

   Discuss model compression, quantization-aware training, pruning, and using lightweight architectures like MobileNet; also mention profiling and iterative testing.

2. 6

   Your model's inference latency is too high on the target hardware. How do you debug and improve it?

   Suggest profiling tools, analyzing bottlenecks (compute vs. memory), optimizing operators, batch size tuning, and using hardware-specific libraries.

3. 7

   How would you design a pipeline to continuously train and deploy models with new data?

   Cover data versioning, automated retraining, CI/CD for ML, monitoring for drift, and rollback strategies; mention tools like Kubeflow or MLflow.

4. 8

   Given a new hardware architecture, how would you port an existing PyTorch model to run efficiently?

   Explain steps: understanding hardware specs, using compiler toolchains, mapping operations to hardware primitives, and validating performance and accuracy.

Coding and Algorithms

1. 9

   Implement a function to compute the convolution of a 2D image with a kernel.

   Write efficient code using loops or vectorized operations; discuss time complexity and potential optimizations like separable convolutions.

2. 10

   Write a function to perform matrix multiplication and optimize it for cache efficiency.

   Consider blocking/tiling, loop reordering, and using libraries like BLAS; explain how cache misses affect performance.

3. 11

   Given a list of numbers, find the top k frequent elements. How would you handle large datasets?

   Use a heap or hash map; for large data, discuss streaming algorithms or MapReduce; mention time and space complexity.

4. 12

   Implement gradient descent for a simple linear regression model from scratch.

   Show update rule, cost function, and convergence check; discuss learning rate selection and vectorization for efficiency.

Behavioral and HR

1. 13

   Why do you want to work in AI and deeptech, specifically at a company focused on hardware acceleration?

   Show passion for the intersection of hardware and ML, mention specific interests like performance optimization or novel architectures, and align with company mission.

2. 14

   Describe a project where you had to learn a new technology quickly. How did you approach it?

   Use STAR method: highlight research, hands-on experimentation, and application; emphasize adaptability and self-learning.

3. 15

   Tell me about a time you worked in a team to solve a complex problem. What was your role?

   Focus on collaboration, communication, and your specific contribution; mention challenges and how you overcame them.

4. 16

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

   Align career goals with company growth, show commitment to deep technical expertise, and express interest in leadership or specialization.