Technical Fundamentals & Core Skills Topics
Core technical concepts including algorithms, data structures, statistics, cryptography, and hardware-software integration. Covers foundational knowledge required for technical roles and advanced technical depth.
Multi Objective Optimization Concepts
Study of problems that require optimizing multiple competing objectives simultaneously, focusing on both theory and practical product trade offs. Core concepts include Pareto optimality, Pareto frontier and dominance relations; scalarization approaches such as weighted sums and Lagrangian methods; constrained formulations; and algorithmic families including multi objective evolutionary algorithms and multi objective gradient methods. Candidates should be able to reason about normalization and weighting of heterogeneous metrics, how to construct utility functions and select operating points on the Pareto frontier, and how to evaluate trade offs through offline simulation and online experimentation. Practical issues covered include sensitivity analysis, fairness and safety constraints, design of multi objective experiments, and computational trade offs when optimizing at scale.
Data Structures and Complexity
Comprehensive coverage of fundamental data structures, their operations, implementation trade offs, and algorithmic uses. Candidates should know arrays and strings including dynamic array amortized behavior and memory layout differences, linked lists, stacks, queues, hash tables and collision handling, sets, trees including binary search trees and balanced trees, tries, heaps as priority queues, and graph representations such as adjacency lists and adjacency matrices. Understand typical operations and costs for access, insertion, deletion, lookup, and traversal and be able to analyze asymptotic time and auxiliary space complexity using Big O notation including constant, logarithmic, linear, linearithmic, quadratic, and exponential classes as well as average case, worst case, and amortized behaviors. Be able to read code or pseudocode and derive time and space complexity, identify performance bottlenecks, and propose alternative data structures or algorithmic approaches to improve performance. Know common algorithmic patterns that interact with these structures such as traversal strategies, searching and sorting, two pointer and sliding window techniques, divide and conquer, recursion, dynamic programming, greedy methods, and priority processing, and when to combine structures for efficiency for example using a heap with a hash map for index tracking. Implementation focused skills include writing or partially implementing core operations, discussing language specific considerations such as contiguous versus non contiguous memory and pointer or manual memory management when applicable, and explaining space time trade offs and cache or memory behavior. Interview expectations vary by level from selecting and implementing appropriate structures for routine problems at junior levels to optimizing naive solutions, designing custom structures for constraints, and reasoning about amortized, average case, and concurrency implications at senior levels.
Graph Algorithms and Traversal
Covers fundamental representations, traversal techniques, and classical algorithms for graph structured data. Candidates should understand graph representations such as adjacency list and adjacency matrix and the tradeoffs in time and space for each. Core traversal skills include implementing and reasoning about breadth first search and depth first search for reachability, traversal order, and unweighted shortest path discovery, as well as tree traversal variants and their relationship to graph traversals. Algorithmic topics include cycle detection, topological sorting for directed acyclic graphs, connected components and strongly connected components, and shortest path and pathfinding algorithms for weighted graphs including Dijkstra algorithm and Bellman Ford algorithm with discussion of negative weights and appropriate use cases. Candidates should be able to analyze time and space complexity, choose appropriate auxiliary data structures such as queues, stacks, priority queues, and union find, handle directed versus undirected and weighted versus unweighted graphs, discuss implementation details and trade offs, and explain practical applications such as dependency resolution, scheduling, pathfinding, connectivity queries, and roles of graph algorithms in system design and data processing.
Trees and Graphs
Comprehensive knowledge of tree and graph data structures and algorithms commonly tested in coding interviews. Candidates should understand representations such as adjacency list and adjacency matrix and when to use each, and tree representations including n ary trees and binary search trees. Expect to implement and reason about traversals including depth first search and breadth first search, tree traversals such as pre order in order and post order, and level order traversal. Cover algorithms including topological sorting for directed acyclic graphs, cycle detection, connected components, shortest path algorithms such as breadth first search for unweighted graphs, Dijkstra for nonnegative weights, and Bellman Ford for graphs with negative edges, and minimum spanning tree algorithms such as Kruskal and Prim. Include disjoint set union find for connectivity and for use with Kruskal, lowest common ancestor techniques and implementations, tree dynamic programming problems, serialization and deserialization, reconstruction from traversals, balancing and validation checks for binary search trees and balanced tree concepts, diameter and path sum problems, and common interview patterns such as path finding dependency resolution and structural transformation. Emphasize implementation details and common pitfalls including correct use of visited tracking recursion depth edge cases and disconnected components, and practice articulating time and space complexity tradeoffs and algorithm selection under different constraints.
Numerical Computing and Stability
Focuses on the numerical aspects of machine learning algorithms and how to ensure stable computation. Topics include floating point precision and rounding errors, underflow and overflow, conditioning and ill posed problems, numerically stable matrix factorizations and solvers, gradient scaling and clipping, mixed precision considerations, and diagnostics for identifying numerical issues.
Recursion and Dynamic Programming
Covers recursive problem solving and dynamic programming as core algorithmic techniques. For recursion, understand how functions call themselves, base and recursive cases, the call stack, common patterns such as tree and graph traversals, backtracking, permutations, and detecting and avoiding infinite recursion. For dynamic programming, understand when to apply optimization via memoization and bottom up approaches, recognize optimal substructure and overlapping subproblems, convert naive recursive solutions into memoized or tabulated solutions, and analyze time and space complexity tradeoffs. Familiarity with classic examples such as Fibonacci, longest common subsequence, knapsack, coin change, and path counting is expected. At more senior levels, be able to discuss performance considerations, space optimization, and how DP principles can map onto real systems such as caching strategies, state management, and optimization of workflows or database query plans.
Sorting and Searching Algorithms
Core computer science algorithms for ordering and locating data, including understanding, implementing, and applying common sorting algorithms and search techniques and analyzing their performance. Candidates should know comparison sorts such as merge sort, quick sort, heap sort, insertion sort, selection sort, and bubble sort and understand stability, in place versus out of place behavior, and best average and worst case time and space complexities. They should master binary search and linear search and variations and know when searching requires a different approach. Knowledge should include algorithmic patterns such as divide and conquer and two pointers, selection algorithms such as quickselect and nth element, and non comparison sorts such as counting sort, radix sort and bucket sort when appropriate. Candidates must be able to implement clean iterative or recursive versions, reason about recursion depth and stack usage, explain trade offs between using built in language sort utilities and custom implementations, and choose the right algorithm for a problem based on input size, memory constraints, and stability requirements. Interviewers often assess coding correctness, complexity analysis using big O notation, edge cases, comparator usage for custom ordering, and ability to justify algorithm choices.
Complexity Analysis and Tradeoffs
Evaluating algorithmic complexity and engineering trade offs between time, space, maintainability, and operational cost. Candidates should be able to express complexity using big O notation, reason about amortized cost, identify bottlenecks, compare alternative solutions and explain when to optimize or accept higher complexity. Include discussion of measurement, profiling, and pragmatic trade offs such as caching versus recomputation or memory versus latency.
Intermediate Algorithm Problem Solving
Practical skills for solving medium difficulty algorithmic problems. Topics include two pointer techniques, sliding window, variations of binary search, medium level dynamic programming concepts such as recursion with memoization, breadth first search and depth first search on graphs and trees, basic graph representations, heaps and priority queues, and common string algorithms. Emphasis is on recognizing problem patterns, constructing correct brute force solutions and then applying optimizations, analyzing trade offs between time and space, and practicing systematic approaches to reach optimal or near optimal solutions.