Based on recent candidate experiences and our engineering requirements, the following areas are the core pillars of our evaluation.
5. NLP and Generative Models
This is the cornerstone of the role. You must demonstrate a deep familiarity with Transformers, Large Language Models (LLMs), and Small Language Models (SLMs). We are interested in how you handle context engineering, fine-tuning, and the architecture of generative systems.
Be ready to go over:
- Transformer Architecture – The mechanics of self-attention, positional encoding, and encoder-decoder structures.
- Generative Approaches – Techniques for text generation, retrieval-augmented generation (RAG), and model compression.
- Fine-tuning Strategies – How to adapt pre-trained models (e.g., BERT, GPT variants) to specific clinical domains with limited data.
- Advanced concepts – Knowledge of model efficiency, quantization, or distilling large models into smaller, faster ones.
Example questions or scenarios:
- "Explain how you would fine-tune a foundation model to extract specific clinical entities from unstructured doctor notes."
- "Compare the trade-offs between using a massive LLM versus a fine-tuned SLM for a latency-sensitive application."
- "Walk me through the attention mechanism mathematically."
2. Coding and Tensor Manipulation
We value engineers who are fluent in Python and PyTorch. Interviews often involve live coding that goes beyond standard algorithms; you may be asked to manipulate high-dimensional data structures directly. This tests your intuition for how data flows through a neural network.
Be ready to go over:
- PyTorch/NumPy Proficiency – Slicing, broadcasting, and reshaping tensors without relying on documentation.
- Vectorization – Writing efficient code that avoids loops where matrix operations suffice.
- Data Preprocessing – converting raw text or structured data into model-ready inputs.
Example questions or scenarios:
- "Implement a specific layer of a neural network from scratch using only tensor operations."
- "Given a 3D tensor representing a batch of sequences, how would you mask specific tokens efficiently?"
- "Write a function to compute the pairwise distance between two sets of vectors without using a loop."
3. System Design and Productionization
Building a model is the first step; running it in production is the goal. We evaluate your ability to design systems that are scalable, reliable, and maintainable. This is especially relevant for Senior and Lead roles.
Be ready to go over:
- ML Ops – Strategies for model versioning, monitoring drift, and automated retraining pipelines.
- Scalability – Serving models with high throughput and low latency.
- Experimental Design – How to set up A/B tests or offline evaluations to measure model impact.
Example questions or scenarios:
- "How would you architect a system to process millions of patient records daily?"
- "What metrics would you track to ensure a deployed clinical model isn't degrading over time?"
- "Design a pipeline for continuous delivery of ML models."
