Roadmaps
Interactive learning paths with real prerequisite dependencies. Start with Computer Vision — the complete journey from linear algebra to foundation models.
Math for ML
The mathematics every ML/Research Engineer is expected to know — linear algebra, calculus, probability & statistics, optimization, and information theory.
Python & Scientific Computing
Strong Python plus the scientific stack the labs run on — NumPy, pandas, Matplotlib — and the software-engineering habits that make ML code reliable.
Coding & Algorithms
Both coding interview rounds the labs run: classic data structures & algorithms, and ML-flavored implementation — writing layers, losses, and training loops from scratch.
Core Machine Learning
The classical ML canon interviews still test — the supervised workflow, tree/kernel models, unsupervised learning, ensembles, and rigorous evaluation.
Deep Learning
The general deep-learning core: MLPs, backpropagation, PyTorch, the regularization/normalization toolkit, and the CNN, RNN, and Transformer architectures.
ML Systems & Scaling
The engineering that trains and serves frontier models — GPUs and CUDA, distributed training, parallelism (FSDP/ZeRO), mixed precision, and inference optimization.
How LLMs Are Trained
From raw text to a helpful assistant — the complete, beginner-first path behind models like Claude, Gemini, and GPT: tokens, transformers, pretraining, and RLHF.
Computer Vision
From linear algebra to foundation models — the complete path to mastering modern computer vision.
Research Engineering
The craft of the job: reading and reproducing papers, designing and analyzing experiments, debugging models, and communicating results — the core of a Research Engineer role.