SIG Software Engineer Interview Questions
4+ questions from real SIG Software Engineer interviews, reported by candidates.
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## Problem Given a block of unstructured text (e.g., a financial news article), extract all valid US stock ticker symbols. A ticker is 1-5 uppercase letters. However, not every sequence of uppercase letters is a ticker — you must filter using a provided set of known valid tickers. ```python def find_tickers( text: str, valid_tickers: set[str] ) -> list[str]: """Return list of tickers found, in order of first appearance, deduplicated.""" ... ``` ``` text = "Investors are watching AAPL and GOOGL closely. The FDA approved MRNA." valid_tickers = {"AAPL", "GOOGL", "MRNA", "TSLA"} Output: ["AAPL", "GOOGL", "MRNA"] ``` ## Follow-ups 1. How do you distinguish the ticker `I` (Intelsat) from the word "I" in normal English text? What heuristics help? 2. Tickers can appear with punctuation around them (e.g., "(AAPL)", "AAPL,"). How does your regex handle this? 3. The valid_tickers set has 10,000 entries. Does set lookup remain O(1) in Python? How does this affect overall complexity? 4. A new ticker was listed today and is not yet in `valid_tickers`. How would you keep your ticker list fresh without manual updates?
## Problem Design and implement a hashmap from scratch with custom hashing for a new programming language, supporting insert, delete, and lookup operations. ## Likely LeetCode equivalent No direct match. ## Tags hash_table, design, coding, oa
## Problem You have a movie catalog as a list of movie objects. Implement a filter and ranking engine: - `filter_movies(genre=None, min_rating=None, max_year=None, director=None)` — return movies matching ALL provided criteria. - `rank_by(field, descending=True)` — sort the filtered results by a given field. - `top_n(n)` — return the top N results after filtering and ranking. ```python from dataclasses import dataclass @dataclass class Movie: title: str genre: str year: int rating: float director: str class MovieFilter: def __init__(self, catalog: list[Movie]): ... def filter_movies(self, **criteria) -> 'MovieFilter': ... def rank_by(self, field: str, descending: bool = True) -> 'MovieFilter': ... def top_n(self, n: int) -> list[Movie]: ... ``` ``` catalog = [Movie("Inception","Sci-Fi",2010,8.8,"Nolan"), ...] result = ( MovieFilter(catalog) .filter_movies(genre="Sci-Fi", min_rating=7.0) .rank_by("rating") .top_n(5) ) ``` ## Follow-ups 1. Your `filter_movies` returns a new `MovieFilter` for chaining. How does lazy evaluation help when the catalog has 1 million entries? 2. Add a `search(query)` method that does fuzzy title matching. What algorithm do you use (Levenshtein, trigram)? 3. How would you support OR conditions (e.g., genre=Sci-Fi OR genre=Action)? 4. Persist the catalog in a SQLite database. Rewrite `filter_movies` to generate a SQL `WHERE` clause instead.
## Problem Simulate a battleship-style sea battle game on a grid, detecting hits and misses or computing game state after a sequence of moves. ## Likely LeetCode equivalent No direct match. ## Tags matrix, simulation, coding, oa
What SIG Looks for in Software Engineer Interviews
SIG Software Engineer interviews are calibrated against the level and scope expected of the role. Across 4+ verified candidate reports on LeakCode, the consistent signals interviewers look for: clear problem decomposition before coding, explicit complexity reasoning, structured handling of edge cases, and the ability to articulate trade-offs between two reasonable approaches.
The discriminator between candidates who advance and candidates who do not is rarely the final correctness of the solution. It is the path to the solution: did you ask clarifying questions, did you state your approach before coding, did you handle edge cases without prompting, and did you communicate your reasoning throughout. Reports tagged "no hire" frequently cite a working solution with poor communication; reports tagged "strong hire" cite clear thinking even when the final solution was incomplete.
How To Use This Question Set
Real interview reports are a calibration tool, not a memorization target. Companies update their question pools every 2-4 months; memorizing exact problems risks misleading you when the interviewer uses a variant. The high-leverage use: identify the patterns that appear repeatedly in SIG Software Engineer reports, practice those patterns on similar (not identical) problems, and use the reports to understand the interviewer's typical follow-up depth.
Filter the questions below by round type, difficulty, and recency. Focus first on reports from the past 6-12 months; older reports may reference questions that have since rotated out of SIG's pool. Reports tagged with quantified difficulty (e.g., "medium-hard") are higher-signal than reports without difficulty tags.
Round-by-Round Expectations
SIG Software Engineer loops typically span 4-6 rounds across phone screens and on-site or virtual on-site interviews. The structure varies by company: some run 1 recruiter screen + 1 technical phone + 3-4 on-site rounds; others run 1 recruiter screen + 1 OA + 4-5 on-site rounds. The recruiter screen is logistics and culture-light; the technical phone screen is medium-difficulty coding; the on-site loop covers coding, system design (at L4+ levels), and behavioral rounds.
Each round is designed to surface a specific signal. Coding rounds: correctness, code quality, complexity reasoning, communication. System design rounds: requirements clarification, design judgment, operational thinking. Behavioral rounds: ownership scope, leadership, ambiguity tolerance, conflict navigation. Strong candidates explicitly hit each signal dimension out loud during the round; weak candidates focus only on solving the prompt.
Common Interview Mistakes At This Combination
Reports tagged "no hire" at SIG Software Engineer commonly cite: jumping into code without clarifying requirements, coding silently for 10+ minutes without verbalizing approach, missing edge cases (empty input, single element, very large input, overflow), and producing a working solution that the candidate cannot explain or refactor when probed. Strong candidates avoid these patterns by following a consistent template: clarify, verbalize approach, code with narration, test with examples.
Behavioral and design rounds have their own failure modes. Behavioral: stories that use "we" instead of "I" diluting individual signal, stories with no quantified outcome, defensiveness when probed about failure. Design: not asking clarifying questions, not stating requirements out loud, designing for a single server when the prompt clearly implies scale, ignoring operational concerns (deployment, monitoring, rollback). These show up in roughly half of SIG Software Engineer interview retrospectives on LeakCode.
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