Principal AI Engineer

A beginner course that gives software engineers and students a clear, practical grounding in modern AI: core concepts, training a simple model, neural networks and deep learning, search algorithms, reinforcement learning, agentic AI, modern tools and prompting, and the ethics and hype around it all. It takes about five hours across 14 chapters with playgrounds and assessments, and needs no prior AI background.

• AI
• Machine learning

A course on AI system design interviews, from classic ML systems such as recommendations, ranking and fraud detection to LLM serving costs, RAG, generative AI products and agentic systems, with a repeatable framework and full case studies. It takes about 20 hours across 9 chapters. It suits engineers who can write code and know what a model, an API and a database are, and are facing design rounds that now include AI.

• AI
• System design
• Job search

Accessible write-ups of Google Research work across machine learning, algorithms, systems, health, climate and other sciences, usually linking to the underlying paper. New posts appear most weeks. It is closer to research than production engineering, so it suits students and engineers who want to see where techniques in forecasting, efficient training, retrieval, tool use and generative AI come from before they reach products.

• System design
• Machine learning
• AI

The free, open companion to the Grokking the System Design Interview course, published as a GitHub repository. It teaches a pattern-first approach: learn building blocks such as caching, sharding, replication and queues well enough that any design question becomes a matter of combining them. It includes a seven-step interview framework, 30 patterns, 59 question walkthroughs, company guides, cheat sheets and study roadmaps, all readable in the browser without an account.

• System design
• Job search

A course on object oriented and low level design interviews. It teaches a repeatable method (requirements, actors and use cases, class diagram, code) with UML basics and SOLID, then applies it to 22 case studies such as a parking lot, elevator, ATM, chess, movie booking, Splitwise and an in-memory cache, with runnable code in Python, Java and C++. It suits candidates facing a dedicated OOD or LLD round.

• Job search
• System design
• CS fundamentals

A six-stage path into paid remote AI data work, with a version for data annotation and one for AI trainers. It covers why models need people, the honest picture of pay, project gaps and scams, the guideline and quality skills platforms test, getting accepted, getting paid and keeping records, and moving up into reviewer, evals, data or ML roles. It suits students and graduates who want flexible remote income and read instructions carefully.

• Career
• AI
• Remote work
• Job search