AI Research Scientist, New Grad – Agents & Reinforcement Learning

A foundations course that builds eleven core data structures from the ground up (arrays, matrices, stacks, queues, linked lists, trees and BSTs, hash tables, hash sets, heaps, graphs and tries), then covers sorting, searching, greedy and divide and conquer. Every chapter has practice problems, flashcards and an assessment. It suits students and career switchers whose foundations came from tutorials and who find pattern courses move too fast.

• Job search
• DSA
• CS fundamentals

A browsable practice library of coding problems that can be filtered by pattern, data structure, difficulty and company, so you can drill exactly the weakness you found this week. Patterns include two pointers, sliding window, hash maps, stacks, backtracking and greedy algorithms, with company filters for Google, Meta, Microsoft, Amazon, Oracle and LinkedIn. Free to browse, with an in-browser editor and solutions.

• Job search
• DSA

On-demand mock interviews with an AI interviewer for coding and system design rounds. It asks questions, follows up on your reasoning the way a real interviewer would, and ends with detailed feedback and a custom improvement plan. It suits software engineers preparing for technical interviews who want to practise whenever they are free and repeat sessions until their answers feel natural.

• AI
• Job search
• Career

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

MIT's full undergraduate algorithms course from Spring 2020, free on OpenCourseWare with lecture videos, notes, problem sets, quizzes and exams with solutions, taught in Python by Erik Demaine, Jason Ku and Justin Solomon. It covers data structures, sorting, hashing, trees, graph search, shortest paths and dynamic programming with the reasoning behind each, which is what lets the ideas transfer to new problems.

• DSA
• CS fundamentals
• Programming