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Optiver interview notes, page 2 of 10

Large Enterprises

Most of these Optiver write-ups come from Software Engineers (69), then Data Scientists (22). The most reported rounds are the technical phone screen (65), the onsite coding round (8), the online assessment (5) and the system design round (5).

91 interview notes · updated Aug 2026

Optiver·Software Engineer·Onsite - Coding / Algorithms

Apr 2026
Optiver software engineering interview with a meaty design problem centered around a balloon festival simulation. The whole thing was essentially one big class design question with a tricky physics-inspired wind model baked in. Felt more like a competitive programming problem than a typical coding round.
  • Design and implement a BalloonFestival class that tracks your team's balloons and competitor balloons across time, handles wind field updates using a cumulative altitude-based model, manages balloon stability rules with time thresholds, and returns the sorted names of your stable balloons flying at or above the highest stable competitor balloon at a given timestamp.

“This one took me a few minutes just to parse.”

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Optiver·Software Engineer·Onsite - Coding / Algorithms

Apr 2026
Optiver SWE interview, got a cargo allocation problem that looked like a clean simulation at first but the constraints basically forced you to think about efficient data structures or you'd TLE on the big cases. Pretty gnarly for a single coding round.
  • You have a fixed fleet of cargo planes, each with a departure time and a capacity. Orders arrive one by one, each with a timestamp and a total cargo size. An order can only use planes that depart strictly after the order's timestamp. If the eligible planes have enough combined remaining capacity, accept the order and greedily fill planes in ascending departure order (ties broken by index); otherwise reject it with no state change. Return the allocation for each order. Aim for roughly O((n + m) log n) overall.

“My first instinct was just sort the planes once and for each order binary search for the eligible subset, then iterate through filling greedily.”

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Optiver·Data Scientist·Technical Phone Screen

Apr 2026
Interviewed for a Data Scientist role at Optiver and got hit with a pretty deep code equivalence problem. Not your typical ML or stats question, this was closer to something you'd see in a systems or algorithms round.
  • Given two short programs that each process an integer array of up to 100,000 elements, determine whether they are functionally equivalent for all valid inputs under specified preconditions (such as 32-bit signed arithmetic with overflow defined or prohibited). If not equivalent, produce a concrete counterexample. Also compare their time and space complexities, identify hidden behavioral differences like mutation, ordering stability, overflow, or integer division semantics, and design property-based tests and edge cases to validate your conclusion.

“This one genuinely surprised me coming from a data science interview.”

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Optiver·Data Scientist·Technical Phone Screen

Apr 2026
Optiver Data Scientist interview with a pretty involved algorithmic puzzle about rearranging apartments across floors with minimum moves. The question had multiple layers and I wasn't fully prepared for how deep they wanted to go on the optimality proof.
  • You have apartments distributed across floors 1 through 4 in some initial arrangement, and a target arrangement. Each move repositions one apartment to a different floor, subject to the target's per-floor counts. Design an algorithm that transforms the initial state into the target using the minimum number of moves, and argue why it's optimal.
  • What is the time and space complexity of your algorithm?
  • Is the greedy strategy of fixing Floor 1 first and then proceeding through Floors 2 to 4 always optimal? Either prove it or find a counterexample.
  • If there are multiple ways to reach the minimum number of moves, how would you output one valid sequence of moves?

“I went with a matching approach where you count mismatches per floor and figure out the minimum swaps needed globally rather than floor by floor.”

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Optiver·Data Scientist·Technical Phone Screen

May 2026
Optiver Data Scientist interview with a probability puzzle that sounds deceptively clean but requires some real combinatorial thinking to get right.
  • A bag has 10 red, 10 yellow, and 10 blue counters. You draw counters one at a time at random without replacement until only two colors remain. What is the expected number of counters left in the bag at that point?

“I stared at this for a bit and tried to brute-force the intuition first, which was a mistake.”

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Optiver·Software Engineer·Technical Phone Screen

May 2026
Optiver put me through a pretty intense cognitive exercise for a Software Engineer role, the kind where you're doing rapid-fire task switching and then immediately asked to articulate your own mental process. Not your typical coding screen.
  • You're given a stream of alternating stimuli (digits and colored shapes). For digits, respond with whether the digit is odd or even; for shapes, respond with the shape's color. Do this as fast and accurately as you can, then walk us through the exact mental strategies you used to reduce the cost of switching between tasks, avoid working memory interference, and bounce back from mistakes.
  • Design a two-week practice plan to improve your reaction time and accuracy on this kind of task switching exercise, including specific drills, rest intervals, and how you'd measure progress.

“This was genuinely weird to be asked in an interview.”

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Optiver·Software Engineer·Onsite - Coding / Algorithms

May 2026
Optiver coding round continuing their Opticargo problem series, this time in a streaming/event-driven flavor. The core challenge was keeping a knapsack-style assignment optimal across dynamic flight and cargo additions without replaying the full problem each time.
  • Design a class that processes streaming events for a cargo-to-flight assignment system. The class must support adding flights with capacities, adding cargo items with weights and values, and returning the current best assignment that maximizes total value within per-flight capacity constraints. Updates must be handled incrementally rather than rerunning full optimization each time.
  • What is the per-event time complexity of your incremental approach, and how would you extend the design to handle flight cancellations or mid-stream capacity changes?

“This is basically an online knapsack problem and it's nastier than it looks.”

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Optiver·Data Scientist·Technical Phone Screen

May 2026
Quantitative interview round at Optiver for a Data Science role. The whole thing was basically one extended Fermi estimation problem with follow-ups that got progressively harder to dodge. Less about math, more about whether you can think out loud without falling apart.
  • Estimate how many US homes could be bought with the total annual amount Americans spend on Netflix subscriptions. You have no reference data, so every assumption needs to be built from scratch and justified.
  • You're now told Netflix has roughly 81 million US subscribers. Update your estimate and your confidence interval. Should the interval get wider or narrower, and by how much?
  • If you were making a market on this quantity, what bid and ask would you quote, and how do those prices connect to your 90% interval?
  • Of the remaining assumptions after the subscriber count is given to you, which single unknown would be most valuable to learn exactly, and why?
  • How does the answer change if 'houses' means homes in San Francisco instead of the national median price?
  • If you answered 100 questions like this, how would you check whether your 90% confidence intervals are actually well-calibrated?

“This wrecked me a little at first because I kept wanting to anchor on some number I half-remembered and couldn't.”

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Optiver·Software Engineer·Technical Phone Screen

May 2026
Optiver software engineering interview with a combinatorics problem that sounds deceptively simple until you actually try to count anything. The core of it was a classic constraint-based sequence counting problem dressed up in trading language.
  • You perform exactly 2n trades starting and ending with 0 shares. Each trade is either buying or selling one share, and your position can never go negative. How many distinct valid sequences exist for a given n, and what's an efficient way to compute it?

“This is the Catalan number problem in disguise, which I did eventually recognize, but not before spending a few minutes trying to brute-force small cases and pattern match.”

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Optiver·Software Engineer·Online Assessment (OA)

May 2026
Optiver software engineer interview with a coding problem that sounds deceptively simple until you realize they explicitly ban any date library. Had to implement everything from scratch including leap year logic, which is where most people probably trip up.
  • Given two date strings in YYYY-MM-DD format, return the absolute number of days between them. You cannot use any built-in date or calendar library. You must implement leap year logic yourself using the standard Gregorian calendar rules.

“My first instinct was to just reach for datetime and I had to stop myself.”

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