AI Subject Matter Expert (SME)

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 second volume of Grokking the System Design Interview, aimed at senior and staff (L5/L6) design rounds. It works through harder case studies such as a payment system, flash sale, notification service, unique ID generator, Gmail, Reddit, Google Calendar and a code judging system, then goes beneath them with patterns like quorum, write-ahead logs and vector clocks and deep dives into Dynamo, Cassandra, Kafka, Chubby, HDFS, GFS and BigTable.

• System design
• Backend
• Job search

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

A revision reference for paid AI data work, in 9 chapters and 64 to 66 concepts per edition: what labels, demonstrations, preference data and evals are, the honest picture of pay and contractor work, scam red flags, the guideline and quality skills tests check, annotation types and tools or the AI trainer's craft, getting accepted, a platform comparison, getting paid and moving up. It follows the Sefism AI data and training work roadmap chapter by chapter.

• Career
• AI
• Remote work
• Job search