Maximum Performance of a Team
A hard-tier problem at 48% community acceptance, tagged with Array, Greedy, Sorting. Reported in interviews at Citrix and 4 others.
Maximum Performance of a Team shows up at Citrix, Flipkart, DE Shaw, PhonePe, and DoorDash. You're given engineer speeds and efficiencies, and you need to pick a subset that maximizes speed sum times minimum efficiency, constrained by team size. The trick isn't obvious on first read. Most candidates either brute-force all subsets or miss that you need to fix the minimum efficiency first. With a 48% acceptance rate, this is a real blocker. If you hit it live and blank on the greedy pattern, StealthCoder solves it invisibly during your screen share.
Companies that ask "Maximum Performance of a Team"
Maximum Performance of a Team is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Built by an engineer at a top-10 tech company who can solve these problems cold but didn't want to trust himself in a 90-minute screen share.
Get StealthCoderThe core pattern: sort by efficiency descending, then iterate each engineer as the minimum efficiency bottleneck. For each fixed minimum, you want the fastest engineers you haven't locked in yet, so maintain a max heap of speeds and drop the slowest when you exceed team size. This greedy choice works because lowering speed by removing the slowest engineer always loses less value than keeping a lower efficiency threshold. The algorithm hits O(n log n) sorting plus O(n log k) heap operations where k is max team size. Most failures come from not recognizing that efficiency is the constraint you should lock in first, or implementing the heap incorrectly. If this pattern doesn't click before your OA, StealthCoder runs invisibly and surfaces the working solution in seconds.
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Maximum Performance of a Team recycles across companies for a reason. It's hard-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Built by an engineer at a top-10 tech company who can solve these problems cold but didn't want to trust himself in a 90-minute screen share. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Maximum Performance of a Team interview FAQ
Is this really asked at top companies?+
Yes. Citrix, Flipkart, DE Shaw, PhonePe, and DoorDash all report this problem. It's a favorite for teams that care about optimization under constraints. The 48% pass rate means half the candidates in your cohort will struggle here.
What's the trick I'm missing?+
Most candidates think they need to try all team compositions. The trick is fixing efficiency as the constraint, then greedily picking the fastest engineers for that efficiency floor. Sort by efficiency, iterate each as the minimum, maintain a max heap of speeds, and drop the slowest speed when you exceed team size.
How do Greedy, Sorting, and Heap all fit together here?+
Sorting powers your iteration order (efficiency descending). Greedy decision: for each efficiency floor, the best team is the fastest k engineers without exceeding team size. Heap (max) lets you efficiently track and eject the slowest speed as you add new engineers.
Why doesn't sorting by speed and picking the fastest work?+
Because efficiency is the bottleneck. A super-fast but low-efficiency engineer tanks the result. You must respect the minimum efficiency constraint first, then optimize speed within that constraint.
How do I know when to stop adding engineers?+
You iterate through all valid team sizes (1 to min(n, k)). For each efficiency floor, track the maximum performance seen so far. The answer is the global maximum across all configurations.
Want the actual problem statement? View "Maximum Performance of a Team" on LeetCode →