20 Coding Patterns to Ace FAANG Without LeetCode
By
Ethan Fahey
•
Feb 16, 2026
Imagine a senior ML engineer in 2025 who’s solved 300+ LeetCode problems and can recite “Trapping Rain Water” from memory, yet freezes in a Google interview when a new graph problem appears. The issue isn’t intelligence or effort; it’s pattern recognition. Top-tier interviewers rarely test obscure riddles anymore; they test familiar coding patterns under time pressure, and the candidates who excel are the ones who can quickly map a new problem to frameworks like topological sort, sliding window, DFS, union-find, or tree-based dynamic programming.
Think of these patterns as reusable mental models that compress thousands of questions into a few dozen categories. That’s the lens recruiters and hiring managers increasingly use to assess signal over grind. We’ll lay out a practical, pattern-first roadmap, connecting interview prep to real production systems work in AI and infra. Platforms like Fonzi AI extend that philosophy into hiring itself, pairing structured, pattern-based evaluation with a curated Match Day that connects experienced engineers to salary-transparent, AI-first companies in just 48 hours.
Key Takeaways
Pattern-based thinking can replace endless LeetCode grinding; mastering 20 core interview patterns covers 70%+ of FAANG-style questions in 2025–2026.
These patterns are especially relevant to AI/ML, infra, and LLM-focused roles, from optimizing transformer attention to building streaming pipelines and vector search systems.
Recognizing the right pattern in the first two minutes separates candidates who struggle from those who ace the interview under time pressure.
Fonzi AI helps candidates practice these patterns in context and then get in front of vetted startups and AI-first companies through a structured Match Day event.
Responsible use of AI in hiring (fraud detection, bias-audited evaluation, logistics automation) gives candidates faster, more human recruiter attention, not less.
Pattern 1–5: Core Array & Pointer Techniques Every FAANG Interview Assumes
These five patterns appear in 60–70% of first-round screens at FAANG companies and similar tech giants. If you skip these, you’re leaving points on the table before the interview even starts.
Sliding Window
The sliding window pattern is your go-to for problems involving contiguous subarrays or substrings. Instead of checking every possible subarray (O(n²)), you maintain a dynamic window that expands and contracts as you traverse the array once.
What it solves: Maximum sum subarray of size k, longest substring without repeating characters, minimum window substring
Canonical problems: LeetCode #3, #53, #76
Time complexity: O(n) versus O(n²) brute force
Real-world use: Streaming anomaly detection, sliding time windows in log analytics
Two Pointers
When you have sorted arrays or linked lists and need to find pairs or validate conditions, two pointers moving from opposite ends (or at different speeds) often reduce O(n²) to O(n).
What it solves: Pair with target sum, 3Sum, removing duplicates in-place, container with most water
Key insight: Works on sorted data or when you need to compare elements from both ends
Space complexity: O(1) extra space in most cases
Fast & Slow Pointers
Also known as Floyd’s tortoise and hare algorithm, this pattern uses a fast pointer and a slow pointer moving at different speeds to detect cycles in linked lists or find the middle element.
What it solves: Linked list cycle detection, finding duplicate numbers in an unsorted array, happy number validation
Why infra roles love it: Low-level systems apply comparable traversal logic to manage cyclic data structures, circular buffers, and memory pointer chains.
Time complexity: O(n) with O(1) space
Prefix Sum & Difference Arrays
Precompute cumulative sums so that any subarray sum becomes a simple subtraction. This transforms O(n) per query into O(1) after O(n) preprocessing.
What it solves: Range sum queries, subarray sum equals k, 2D prefix sums for image processing
AI/ML application: Computing attention masks efficiently, batch normalization statistics
Pattern signal: Any problem asking about sums over a specific range
Cyclic Sort
When you have an unsorted array containing numbers in a specific range (1 to n), the cyclic sort pattern places each element at its correct index in O(n) time with O(1) space.
What it solves: Finding the missing number, finding all duplicates, first missing positive
How it works: Swap all the elements to their correct positions, then scan for mismatches
Real-world use: Data validation in large-scale ETL pipelines
Pattern 6–9: Linked Lists, Intervals, and Heaps You Can’t Skip
FAANG and top startups often mix one list or interval problem with one heap or priority queue problem in their interview loops. These patterns test pointer discipline and your ability to manage ordered data efficiently.
In-place Reversal of Linked List
The linked list pattern for reversal manipulates prev/next pointers iteratively, achieving O(1) space. This is foundational for many list transformations.
What it solves: Reverse entire list, reverse nodes in k-group, palindrome linked list check
Key technique: Track previous, current, and next pointers; update in each iteration
Why it matters: Tests pointer discipline and careful state management under pressure
Merge Intervals Pattern
For problems involving intervals, sort by start time and then merge overlapping intervals or insert new ones. The merge intervals pattern handles scheduling, resource allocation, and time-based queries.
What it solves: Merge overlapping intervals, insert interval, meeting rooms II
Real-world analogs: Scheduling GPU jobs, shard assignments, and only mutually exclusive intervals for resource planning
Time complexity: O(n log n) due to sorting, then O(n) for merging
Watch for: Two intervals that touch at boundaries (decide if they count as overlapping or mutually exclusive intervals)
Top K Elements Pattern
When you need the k largest or smallest elements from a collection, a min heap or max heap gives you O(n log k) performance instead of O(n log n) from full sorting.
What it solves: Kth largest element, top k frequent elements, k closest points to origin
Data structure: Use a priority queue, min heap to track k largest, max heap for k smallest
Streaming use case: Real-time log analytics, where you track the smallest element or top k elements as data flows through
Two Heaps Pattern
Maintain a max heap for the lower half of a number stream and a min heap for the upper half. This gives O(log n) inserts and O(1) median queries.
What it solves: Find the median from a data stream, sliding window median
Why it appears: System-design-adjacent coding rounds often test this as a follow-up
Balance rule: Keep heaps within a size difference of 1
Pattern 10–13: Trees, Graphs, and Traversals for Real Systems and ML
Modern AI and infra systems are graph-heavy. Compute graphs power deep learning frameworks. Dependency DAGs drive build systems. Knowledge graphs enable semantic search. If you want to work on real systems, you need these patterns cold.
Binary Tree Traversals (DFS Variants)
Understand when to use preorder, inorder, or postorder traversal. Each serves different purposes in a binary tree.
Inorder: Produces sorted order for BST, useful for validation and range queries
Preorder: Used for serialization, copying trees, prefix expression evaluation
Postorder: Compute subtree values bottom up, delete trees, evaluate postfix expressions
Path problems: Track accumulated values down paths to find sums or validate constraints
BFS (Tree/Graph)
Breadth-first search explores all the nodes at the current level before moving deeper. Use a queue data structure for level order traversal.
What it solves: Shortest path in unweighted graphs, binary tree level order traversal, word ladder, rotten oranges
AI/ML use: Model-serving dependency resolution, finding shortest paths in knowledge graphs
Pattern signal: “Minimum steps,” “shortest path,” or “level by level”
DFS (Tree/Graph)
Depth-first search explores deep paths before backtracking. It underpins backtracking, cycle detection, and connected component counting.
What it solves: Number of islands, detect cycle in graph, path sum, all paths from source to target
Implementation: Recursive or iterative with explicit stack
Time complexity: O(V + E), where V is vertices and E is edges
Topological Sort
For any directed acyclic graph (DAG), topological ordering produces a linear sequence respecting dependencies. Kahn’s algorithm uses indegree counting with a queue.
What it solves: Course schedule, task scheduling with prerequisites, and build system ordering
AI/ML use: Pipeline step ordering, dependency resolution in compute graphs
Pattern signal: “Prerequisites,” “ordering tasks,” “dependencies”
Pattern 14–16: Backtracking, Subsets, and Combinatorics (Often Seen in LLM/AI Roles)
Search and combinatorial explosion problems show up in ML research, LLM reasoning systems, and constraint solvers. These patterns help you navigate exponential search spaces efficiently.
Backtracking
The subsets pattern and backtracking approach explore decision trees by making a choice, recursing, and reverting if the path fails. Pruning invalid branches early keeps complexity manageable.
What it solves: N-Queens, Sudoku solver, generating permutations, and combination sum
LLM application: Search over prompt structures, constraint satisfaction in reasoning
Template: Choose → explore → un-choose (backtrack)
Subsets / Combinatorial Generation
Generate all subsets, combinations, or permutations of a given array. This models feature subsets in ML experiments, A/B test groupings, or exhaustive search spaces.
What it solves: Subsets, subsets II (with duplicates), permutations, combinations
Complexity: O(2^n) for subsets, O(n!) for permutations, know when this is acceptable
Optimization: Use bitmasks for compact representation when working with new array generation
Matrix / Grid Traversal
DFS or BFS over 2D grids handles spatial problems. Mark visited cells to avoid cycles and track paths or components.
What it solves: Number of islands, flood fill, shortest path in a binary matrix, word search
AI/ML connection: Image segmentation regions, attention mask patterns, game map pathfinding
Common mistake: Forgetting to check bounds or mark cells as visited
Pattern 17–20: Dynamic Programming, Search, and Optimization for Large Data
Senior FAANG and top AI roles often test dynamic programming and optimized search on large inputs (10⁵–10⁶ elements). These patterns demonstrate algorithmic maturity and the ability to solve problems at scale.
Classic DP (1D and 2D)
Identify overlapping subproblems and optimal substructure. Build solutions bottom up using tabulation or top down with memoization.
What it solves: Knapsack variants, longest increasing subsequence, edit distance, coin change
Pattern signal: “Maximum/minimum value,” “number of ways,” “can you partition”
Implementation: Define state, recurrence relation, base cases, then fill the table
DP on Trees and Graphs
Compute values bottom up on DAGs or trees. Each node’s answer depends on its children’s computed values.
What it solves: Tree DP for subtree sums, house robber III, longest path in DAG
AI/ML use: Compiler optimization on compute graphs, model architecture search
Approach: Post-order traversal, where each node aggregates children’s results
Modified Binary Search Pattern
The modified binary search adapts standard binary search for rotated arrays, unknown array sizes, or parametric search, where you find the minimal feasible answer.
What it solves: Search in a rotated sorted array, find the minimum in a rotated array, and capacity to ship packages
Real-world use: Performance tuning thresholds, capacity planning, finding optimal batch sizes
Key insight: Binary search works whenever the search space is monotonic in some property
Union-Find (Disjoint Set Union)
Track connected components with near O(1) amortized operations using path compression and union by rank. This pattern enables the efficient merging of individual objects into groups.
What it solves: Number of provinces, redundant connections, accounts merge, Kruskal’s MST
AI/infra use: Clustering user graphs, deduplication systems, distributed system partitioning
Operations: Find (with path compression) and Union (with rank optimization)
How to Recognize the Right Pattern in the First Two Minutes
Top candidates don’t start coding immediately. They map the problem to a pattern first, usually within 60–120 seconds. This is how they solve problems they’ve never seen before.
Quick mapping checklist:
Data shape: Is the input an array, a binary tree, a graph, a grid, or linked lists?
Question type: Are you counting, finding a path, optimizing a value, or checking feasibility?
Constraints: Is n up to 10³ (brute force okay), 10⁵ (need O(n) or O(n log n)), or 10⁶+ (must be near-linear)?
Ordering: Does contiguity matter? Is the input in sorted order?
Pattern signals to memorize:
Contiguous ranges or windows → sliding window or prefix sum
Sorted data + search → binary search or two pointers
Dependencies or ordering → topological sort
Connected components → BFS, DFS, or union-find
Optimal substructure + overlapping subproblems → dynamic programming
Build a pattern index document:
List each pattern with 1–2 canonical problems and their time and space complexity
Review it weekly until pattern recognition becomes automatic
Track which patterns you routinely misidentify (e.g., confusing backtracking vs. DP on subsets)
Practice saying the pattern out loud:
In mock AI interviews, explicitly state your hypothesis before coding: “I think this is a sliding window with hash maps because we need contiguous subarrays with unique elements.”
This demonstrates structured thinking and gives interviewers insight into your reasoning
How AI Is Changing Technical Hiring and Where Fonzi Fits
By 2025–2026, most serious tech companies will use AI somewhere in their hiring stack. The question is whether that AI helps candidates or hurts them.
Common (problematic) uses of AI in hiring:
Resume keyword filters that reject qualified candidates for superficial reasons
Automated video scoring that penalizes accents or non-standard speech patterns
Passive data scraping that creates opaque candidate profiles without consent
These systems often reinforce bias and leave candidates confused about why they were rejected
Fonzi AI’s approach is different:
AI handles infrastructure and integrity: fraud detection, duplicate profile identification, scheduling automation, and structured resume evaluation summaries
Humans make all final decisions about candidate fit and company matching
The development process for these tools prioritizes transparency and candidate experience
Bias-audited evaluation:
Structured rubrics for assessing pattern knowledge, communication, and trade-off reasoning
Anonymized early-stage reviews that focus on skills rather than demographic signals
Periodic audits to prevent drift against underrepresented candidates
This approach uses software design techniques that compose objects of evaluation into fair, consistent assessments
What this means for candidates:
Less guesswork: you know salary bands upfront, role expectations, and whether the interview is primarily focused on DP + graph patterns or system design
Faster process: AI scheduling and logistics mean you spend time on performance, not admin
Human-centered outcomes: responsible AI makes recruiters more effective, not obsolete
Inside Fonzi Match Day: Fast-Track Your Pattern Skills into Real Offers
Imagine a 48-hour window where curated AI/ML, infra, and full-stack engineers are introduced to vetted AI startups and high-growth tech companies. That’s Match Day, and it compresses months of job search into days.
Before Match Day:
Apply once with your resume and GitHub/portfolio
Complete a focused evaluation on core patterns and system reasoning, not endless generic question banks
Your profile is curated and enhanced, not just keyword-matched
During the 48-hour event:
Companies review curated profiles and send interview requests
They commit to salary ranges before the first conversation—no more guessing games
A broader range of opportunities becomes visible because companies trust the curation
Interview logistics handled:
Fonzi’s concierge team with AI scheduling tools manage time zones, video links, and prep materials
You focus on demonstrating your pattern knowledge and system design skills
No more juggling 15 recruiter emails and calendar conflicts
Typical outcomes:
Many candidates move from first conversation to offer in under 7–10 days
Compare that to traditional multi-month hiring funnels with endless take-homes
The structure creates accountability on both sides
20 Coding Patterns and Where They Show Up in Real Work
Senior interviewers think in patterns when they write questions. Understanding this mapping helps you see interview questions the way they do, as instantiations of familiar templates applied to specific domains.
Pattern | What It Solves | Example Interview Problem | Real-World AI/Infra Use Case |
Sliding Window | Contiguous subarray/substring optimization | Maximum Sum Subarray, Longest Substring Without Repeating | Streaming anomaly detection, rolling metrics |
Two Pointers | Pair finding, in-place modifications on sorted data | 3Sum, Container With Most Water | Log deduplication, sorted list merging |
Fast & Slow Pointers | Cycle detection, middle finding | Linked List Cycle, Happy Number | Dependency cycle detection, identifying infinite loops in state machines |
Prefix Sum | Range sum queries, subarray counting | Subarray Sum Equals K, Range Sum Query 2D | Attention mask computation, batch statistics |
Cyclic Sort | Finding missing/duplicate in 1-n range | Find Missing Number, Find All Duplicates | Data validation in ETL pipelines |
In-place Reversal | Linked list transformations | Reverse Linked List, Reverse Nodes in k-Group | Memory-efficient list processing |
Merge Intervals | Overlapping interval consolidation | Merge Intervals, Meeting Rooms II | GPU job scheduling, shard assignments |
Top K Elements | Finding k largest/smallest efficiently | Kth Largest Element, Top K Frequent | Real-time log top-k, recommendation ranking |
Two Heaps | Running median, balanced partitions | Find Median from Data Stream | Streaming percentile tracking |
Tree DFS (Pre/In/Post) | Path sums, serialization, BST operations | Binary Tree Maximum Path Sum, Serialize Tree | Model checkpoint traversal, AST processing |
BFS (Tree/Graph) | Level-order traversal, shortest unweighted path | Binary Tree Level Order Traversal, Word Ladder | Service dependency resolution, BFS over knowledge graphs |
DFS (Graph) | Connected components, cycle detection, paths | Number of Islands, All Paths Source to Target | Cluster discovery, dead code detection |
Topological Sort | DAG ordering respecting dependencies | Course Schedule, Alien Dictionary | ML pipeline builds, task scheduling |
Backtracking | Constraint satisfaction, exhaustive search | N-Queens, Sudoku Solver | Prompt structure search, configuration generation |
Subsets/Permutations | Combinatorial generation | Subsets, Permutations | Feature subset search, A/B test groupings |
Grid Traversal | 2D spatial problems | Number of Islands, Shortest Path in Binary Matrix | Image segmentation, game pathfinding |
Classic DP | Optimization with overlapping subproblems | Knapsack, Longest Increasing Subsequence | Resource allocation, sequence alignment |
Tree/Graph DP | Bottom-up computation on hierarchical structures | House Robber III, Longest Path in DAG | Compute graph optimization, model architecture search |
Modified Binary Search | Search in transformed/unknown spaces | Search in Rotated Array, Capacity to Ship | Capacity planning, batch size optimization |
Union-Find | Dynamic connectivity, component tracking | Number of Provinces, Accounts Merge | User graph clustering, distributed partition management |
Preparing for FAANG and AI-First Interviews Without Burning Out
Sustainable prep matters, especially for staff-level or senior engineers already working full-time. Here’s how to structure your preparation without sacrificing sleep or sanity.
6–8 week plan:
Cover 3–4 patterns per week
Solve 3 representative problems for each pattern (one easy, one medium, one hard)
Schedule 1 mock interview on weekends to practice pattern identification under pressure
Targeted practice:
Track which patterns you routinely misidentify in a spreadsheet
Focus subsequent weeks on weak areas rather than grinding comfortable patterns
Use object-oriented programming principles when implementing solutions—clean code impresses interviewers
Balance algorithm prep with role-specific topics:
For staff+ roles: allocate 40% time to system design, 40% to algorithms, 20% to domain knowledge
For AI/ML roles: add vector search, distributed training, and LLM inference optimization
For infra roles: emphasize networking, storage systems, and computer science fundamentals
Let Fonzi bridge prep and opportunities:
Apply once with your profile and complete the evaluation when ready
Join Match Day when your prep reaches a strong baseline
Skip the grind of sending 100+ cold applications to companies that ghost you
Conclusion
If you zoom out, mastering 20 core coding interview patterns is far more powerful than grinding through 500 random LeetCode problems. These patterns consistently show up in FAANG and AI/infra interviews because they mirror real engineering work, designing systems that process data efficiently, manage dependencies, and scale reliably. For recruiters and engineering leaders, pattern fluency is a far stronger signal of on-the-job performance than raw problem volume.
The same principle applies to hiring infrastructure. Responsible AI should make recruiting faster, fairer, and more transparent, not more opaque. Fonzi AI is built around that idea: AI streamlines logistics and integrity checks, humans make the final decisions, and candidates see clear salary and role expectations upfront. Instead of turning interviews into a lottery, Fonzi pairs high-signal, pattern-based evaluation with structured Match Days, helping AI engineers connect directly with salary-transparent, AI-first companies that value real systems thinking.
