Maximum Profit in Job Scheduling
A hard-tier problem at 54% community acceptance, tagged with Array, Binary Search, Dynamic Programming. Reported in interviews at Pony.ai and 20 others.
Maximum Profit in Job Scheduling hits your OA from 21 companies including Airbnb, DoorDash, Pinterest, and Snowflake. You're given jobs with start time, end time, and profit. Pick a non-overlapping subset to maximize earnings. It looks like a greedy problem but greedy fails. The acceptance rate sits at 54%, which means half the candidates either time out or miss the DP insight entirely. If you blank on the pattern during the assessment, StealthCoder surfaces the working approach invisible to the proctor.
Companies that ask "Maximum Profit in Job Scheduling"
Maximum Profit in Job Scheduling 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 who got tired of watching his cohort grind for six months and still get filtered at the OA stage.
Get StealthCoderThe trap is sorting by end time and greedily picking highest-profit jobs. That doesn't work because a low-profit early job can block two high-profit jobs later. The real pattern: sort by end time, then use dynamic programming with binary search to find the latest non-conflicting job. dp[i] equals the max profit using jobs 0 to i. For each job, you either skip it or take it and add its profit to the best solution that ends before it starts. Binary search finds that cutoff in log time. Array, Binary Search, Dynamic Programming, and Sorting all tie together here. Most candidates see DP and miss the binary search piece, or build the DP but time out on the job-lookup step. StealthCoder is the hedge when the live OA pressure makes you second-guess the approach.
Pattern tags
You know the problem.
Make sure you actually pass it.
Maximum Profit in Job Scheduling 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 who got tired of watching his cohort grind for six months and still get filtered at the OA stage. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Maximum Profit in Job Scheduling interview FAQ
Do I really need binary search for this, or will linear search pass?+
Linear search works algorithmically but time limit usually forces binary search. With up to 5 * 10^4 jobs, the O(n log n) sort plus O(n log n) DP with binary search is the expected path. Pure O(n^2) DP fails on large inputs. The problem is asking for the optimized version.
Is this still asked at top companies or is it too well-known now?+
Still asked frequently. Airbnb, DoorDash, Pinterest, and Snowflake all report it in recent cycles. It's hard enough that knowing the name doesn't guarantee you'll code it fast under pressure. Companies haven't rotated it out.
What's the trick I'm missing if greedy feels right?+
Greedy fails because a job with low profit but early end time can unlock two high-profit jobs later. You can't decide locally which jobs to take. DP forces you to consider all valid subsets, which greedy can't model.
How does binary search fit into this problem?+
After sorting by end time, for each job you need to find the latest previous job that doesn't conflict with it. Binary search on the sorted array finds that index in log time instead of scanning linearly. That's the difference between passing and timing out.
Will knowing Array, Binary Search, Dynamic Programming, and Sorting separately be enough?+
Knowing them separately is not enough. This problem forces you to integrate all four. You sort the array, build a DP table, use binary search inside the DP loop. Integration under time pressure is where most candidates stumble.
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