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Series B+ Startup·Software Engineer·Online Assessment (OA)·Junior

JuniorRejected
Jun 2026Remote

Summary

Took an OA for a fintech company in the US as a PhD math student with no internship experience. The test was two LC hards and two LC mediums in 100 minutes on HackerRank, and I only solved one hard and one medium. Came out feeling pretty wrecked, and the anxiety leading up to it didn't help.

Questions Asked (2)

Q1

Solve a hard algorithmic problem requiring a combination of backtracking and dynamic programming or greedy strategies.

Algorithms & Data Structures
Author's notes

This was the one that broke me.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and constraints, then discuss brute force and identify overlapping subproblems or greedy choice properties. Propose a combined approach (e.g., DP with backtracking for reconstruction) and analyze time/space complexity, optimizing if needed.

Pro tip: Verbalize your thought process and trade-offs; interviewers value clear reasoning over silent coding. If stuck, simplify the problem and build up, showing adaptability.

1. Understand and Clarify

Ask questions to confirm input/output, constraints, and edge cases. Restate the problem in your own words to ensure alignment.

2. Explore Approaches

Discuss brute force and identify patterns like overlapping subproblems or greedy choices. Compare DP, backtracking, and greedy strategies.

3. Design Combined Solution

Outline how to combine techniques, e.g., use DP for optimal value and backtracking to reconstruct solution. Define state, transitions, and base cases.

4. Analyze Complexity

Derive time and space complexity, and discuss potential optimizations like memoization or pruning.

5. Implement and Test

Write clean code with meaningful variable names, then walk through examples and edge cases to verify correctness.

Key Points to Mention

  • Time and space complexity analysis with Big-O notation
  • Trade-offs between DP, backtracking, and greedy approaches
  • Optimal substructure and overlapping subproblems
  • Memoization vs. tabulation in dynamic programming
  • Pruning and state space reduction in backtracking
  • Edge cases and constraints handling

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Q2

Solve a medium-difficulty algorithmic coding problem under timed conditions.

Algorithms & Data Structures
Author's notes

Managed to get through this one, which was the small win of the day.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then walk through a brute-force solution before optimizing. Use a structured approach: understand, plan, code, test, and analyze complexity. Communicate your thought process clearly throughout.

Pro tip: Don't rush to code; spend the first few minutes ensuring you fully understand the problem and discussing trade-offs. Interviewers value clear reasoning and the ability to identify optimal solutions over immediate coding.

1. Understand the Problem

Ask clarifying questions about input size, constraints, edge cases, and expected output. Restate the problem in your own words to confirm understanding.

2. Explore Approaches

Discuss a brute-force solution first, then identify bottlenecks and propose optimizations using appropriate data structures or algorithms. Compare time and space complexity.

3. Plan the Code

Outline the steps or pseudocode before writing actual code. Break the problem into smaller sub-problems and decide on variable names and structure.

4. Implement and Test

Write clean, modular code while explaining your logic. Test with provided examples and edge cases, and debug systematically if issues arise.

5. Analyze and Reflect

State the final time and space complexity. Discuss potential improvements or alternative solutions, and reflect on any trade-offs made.

Key Points to Mention

  • Time and space complexity analysis for each approach
  • Edge cases such as empty input, large input, duplicates, or negative numbers
  • Choice of data structures (e.g., hash maps, heaps, trees) and why they are optimal
  • Trade-offs between different solutions (e.g., readability vs. performance)
  • Testing strategy including unit tests and boundary conditions
  • Communication of thought process and ability to adapt to feedback

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.