OpenAI · Software Engineer (New Grad)
OpenAI's new-grad SWE loop pairs a strong classical coding bar with a bias toward practical, ship-it engineering rather than pure puzzle-solving. Expect medium-hard algorithm and data-structure rounds, at least one hands-on/practical round in a real editor, and pointed discussion of a project you actually built end to end. Because the work sits close to large-scale ML systems, interviewers reward candidates who reason about correctness, scale, and what breaks when data or load grows.
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What this interview tests
- Algorithmic depth (graphs, heaps, DP, string/array manipulation)
- Practical coding in a real editor (extend a service, process data)
- Correctness, scale, and failure reasoning at high throughput
- Big-O analysis and stating trade-offs before coding
- Depth and ownership on one shipped end-to-end project
- Curiosity about large-scale ML systems
Common question themes
Graph and shortest-path problems with follow-up optimization
Dynamic programming (sequences, partitions, grids)
Process or aggregate a large data stream in a real editor
Debug a failing test in an unfamiliar codebase
"Walk me through a system you built and the hardest trade-off"
"How would this hold up at 100x the data or traffic?"
Modeled on a public OpenAI new-grad SWE posting