For classical ML practitioners, gradient boosted trees, SVMs, feature engineering pipelines, the modern MLE interview circuit can feel like a moving target. The fundamentals haven't gone away, but a new layer has settled on top: big tech companies increasingly expect candidates to reason about, and implement components of large language models from scratch.
This series is a bridge for classical ML practitioners helping you fill the transformer gap in your knowledge. This article is derived from my own self-study preparing for interviews, where I discovered many transformer concepts are much easier to understand when framed in the context of fundamental concepts from classical ML.
Machine learning engineering roles span a wide range of skillsets, and the interview process reflects that. A perfect MLE is sometimes related to a unicorn, where none truly exists (except potentially Andrej Karpathy). We are expected to know all of classical ml, up-to-date knowledge on the evolving LLM transformer architecture, deployment strategies, ML systems design, distributed training expertise, data expertise, product expertise, agent expertise, ect. in addition to the most relevant skills to the job understanding the core quality iteration process, and core software-engineering skills; it can feel overwhelming.
In this multi-part article, I will try to cover these subjects in enough depth to meaningfully assist in your interviews, placing transformer concepts in context of classical ML concepts.
In this part 0, we will map the terrain: what the interview process actually looks like today, where LLM implementation fits in, and what we'll cover in subsequent posts.
Part 0. Overview Part 1. Attention & Positional Embeddings Part 2. Transformer Block , Encoder & Decoder Part 3. Deep learning fundamentals - AdamW, & Training Stability Part 4. Deep learning fundamentals - Self-Supervision, Cross Entropy & the Training Loop Part 5. PyTorch fundamentals - Implementing every Transformer component from scratch
Appendex Part A.1 - Distributed Pre-Training & Identifying Bottlenecks in Distributed Systems Part A.2 - RL-Fine-Tuning Part A.3 - Post-Training and Fine-tuning Part A.4 - Transformers beyond language, implementing an audio-transformer
The Modern ML Engineer Interview
ML Theory / Concepets Round
This round has historically focussed on classical ML, for example questions like:
- Walk me through how gradient boosting works. How does it differ from random forests?
- What is the bias-variance tradeoff? How does regularization affect it?
- Why do we use cross-entropy loss for classification instead of MSE?
- What are the assumptions behind logistic regression?
- Explain dropout — what problem does it solve, and what does it do at inference?
However, more recently this round has began to place more emphasis on Transformer concepts. Example questions include:
- When would you use data-parallel vs pipeline-parallel distributed training for a LLM? What factors lead to the decision?
- Explain the tradeoffs between RoPE and sinusoidal positional embeddings. When would you prefer one over the other?
- Compare the tradeoffs between scaled dot-product attention, multi-head attention, latent attention and grouped-query attention. Why has Latent Attention & Grouped Query Attention become common in modern open source LLMs?
- You might be asked to compare batch normalization and layer normalization, and to explain why transformers use layer norm instead of batch norm.
4. ML System Design
A 45–60 minute open-ended design problem. Common prompts include:
- Design a search ranking system for an e-commerce platform.
- Design a recommendation system for a short-form video feed.
- Design a content moderation pipeline.
- Design a system that detects anomalies in time-series sensor data.
At LLM-focused companies, the prompt is increasingly likely to involve an LLM in some capacity: "Design an inference API for serving large language models. Variable-length requests, GPU memory management across concurrent requests, request queuing with priority, streaming responses" or "Design a fine-tuning pipeline for adapting a foundation model to our domain."
5. ML Implementation / Coding from Scratch
This is the round that has changed most dramatically in the last two years. Rather than — or in addition to — asking you to implement a logistic regression or k-means from scratch, interviewers are now asking candidates to implement components of the transformer architecture.
Common asks:
- Implement scaled dot-product attention in NumPy or PyTorch.
- Implement multi-head attention.
- Implement sinusoidal positional encodings.
- Write a simple autoregressive sampling loop.
- Implement a basic key-value cache for inference.
- Explain and implement layer normalization.
The expectation isn't that you'll produce production-quality code in 45 minutes. The expectation is that you can reason about why each piece exists, handle the tensor shapes correctly, and talk through tradeoffs as you code.
Why LLM Implementation Has Entered the Interview
The shift isn't arbitrary. Language models have become infrastructure. At companies building AI products — and increasingly at companies where AI is a component of a larger product — MLEs are expected to work directly with transformer-based systems. That means debugging training instabilities, reasoning about memory consumption during fine-tuning, understanding what a KV cache is and when it helps, and reading architecture papers well enough to adapt them.
The interview is catching up to the job.
There's also a signal-quality argument. Classical ML implementation questions (k-means, PCA, linear regression) have been so thoroughly covered by prep resources that they've become pattern-matching exercises. Transformer component implementation is harder to grind without genuine understanding. A candidate who can implement multi-head attention from scratch and explain why the keys, queries, and values are projected separately almost certainly understands attention — not just a memorized recipe.
What This Series Covers
This is Part 0. The subsequent posts will build a working transformer from the ground up, explaining each decision in terms of the classical ML intuitions you already have. The rough roadmap:
- Part 1: The attention mechanism — from similarity search to softmax-weighted retrieval
- Part 2: Positional encodings — why sequence order isn't free, and how to inject it
- Part 3: Multi-head attention and the transformer block
- Part 4: Training considerations — layer norm, residual connections, and why they matter
- Part 5: Autoregressive decoding and KV caching
- Part 6: Putting it together — a minimal GPT in ~200 lines
Each post will include implementation exercises formatted the way interviewers actually ask them, with commentary on what a strong response looks like and where candidates typically stumble.
If you've spent time in classical ML and feel like the transformer revolution happened in a language you don't quite speak yet — this is the series for you.