Hudson River Trading SWE Interview: Coding Screen Guide

Estimated read time: 7-9 minutes

Summary: The Hudson River Trading SWE coding screen is an engineer-led technical gate, commonly reported around 45-60 minutes. The source supports data structures and algorithms, array/hash-map coding, C++/Python implementation, runtime and memory analysis, and edge cases under follow-up constraints. Exact question wording is weak, so prepare for rigorous fundamentals and role-specific follow-ups rather than memorized questions.

TL;DR + FAQ (read this first)

At-a-glance takeaways

• The coding screen is commonly reported around 45-60 minutes.
• Expect data structures, algorithms, language implementation, complexity, memory, and edge cases.
• C++/Python and systems fundamentals may matter more for some HRT role families.
• Do not treat quant puzzle or Algo Developer reports as universal SWE evidence.
• Communication and correctness under follow-ups matter.

Quick FAQ

Is this just DSA?
Mostly coding-heavy evidence, but implementation language, systems fundamentals, and performance follow-ups may appear by role.

Should I expect low-level systems?
Possibly for Core Developer, infrastructure, or systems-heavy roles. Confirm with the recruiter.

Are exact questions known?
No. Public evidence is mostly themes and role-mixed reports.

What matters most?
Correct efficient code, clear reasoning, edge cases, and complexity.

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1) What the coding screen measures

The coding screen measures whether you can solve a problem correctly and efficiently while communicating your reasoning. HRT's public reports point to coding-heavy interviews, strong fundamentals, edge-case handling, and runtime/memory analysis.

For HRT, implementation details can matter. A solution that is asymptotically fine but sloppy with memory, data layout, or language behavior may invite follow-ups, especially in systems-heavy roles.

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2) How HRT-style coding may feel

A question may start as a standard DSA task, then add performance, memory, or language constraints. An array task can become a streaming data task. A hash-map solution can add high-volume inputs. A Python solution can trigger a discussion about implementation cost. A C++ solution can invite questions about ownership, copying, or undefined behavior.

When constraints change, restate what changed and adjust the design deliberately. HRT interviews reward fast reasoning, but not reckless patching.

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3) Questions to prepare

These are representative tasks based on source themes, not confirmed verbatim HRT questions.

• Given an array of values, return the pair or group satisfying a target condition. Start with a simple solution, then optimize runtime and memory.
• Given a stream of records, maintain an aggregate by key. Now handle repeated records and high input volume.
• Implement a data structure with insert, delete, and query operations. Explain the runtime of each operation.
• Write a C++ or Python solution, then explain where unnecessary allocations, copies, or slow operations could appear.
• Given code with an edge-case failure, identify the failing input and fix it without changing the intended behavior.
• Modify your solution when memory is limited or inputs arrive incrementally.
• Explain the tests you would run for empty input, duplicate keys, large input, and invalid records.

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4) Level-specific expectations

The slug table lists intern through senior as relevant, with Staff+ possible or role-dependent. HRT-specific level labels were not verified.

• Intern and New Grad: focus on fundamentals, correctness, and clean explanation.
• Junior and Mid-Level: show efficient implementation, edge cases, complexity, and language fluency.
• Senior: add systems awareness, performance tradeoffs, and maintainability under follow-ups.
• Staff+: public evidence is weak, so confirm whether coding, systems, or leadership depth dominates.

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5) Common failure modes

Weak fundamentals. Coding-heavy interviews expose gaps quickly.

Ignoring complexity and memory. HRT-style engineering often cares about efficiency.

Borrowing quant reports blindly. Quant and Algo Developer loops may not map to SWE.

Silent coding. Explain assumptions, data structures, and tradeoffs.

No edge-case testing. Duplicates, empty input, large input, and invalid records matter.

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6) How to prepare

• Practice arrays, hash maps, heaps, trees, graphs, and custom data structures.
• Review C++ or Python implementation details relevant to your role.
• Practice adding follow-up constraints around memory, streaming, and performance.
• Explain runtime and memory for every solution.
• Ask the recruiter whether the screen is general SWE or systems-heavy.

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