Over the past stretch I gathered a batch of OpenAI debriefs, and stitching different candidates' experiences together paints a fairly clear picture. It's neither as templated as a traditional big tech loop nor a purely research-flavored chat. More precisely, it's a system that fuses engineering ability, abstraction skill, and a degree of research thinking.
1. Process: Clear Cadence, Opaque Feedback
Most candidates start with a recruiter call. This round is relaxed—mostly team/role intro and a background-fit check—and the experience is generally positive.
Next are usually two technical rounds: one coding, one system design. An easy misread: this stage does NOT emphasize AI/ML-specific prep. The core is still data structures, algorithms, and general system design. Pass that and you reach the onsite, typically coding, system design, a technical deep dive, and a hiring-manager round.
| Stage | Content | Experience |
|---|---|---|
| Recruiter Call | Team / role / background fit | Relaxed, friendly |
| Tech Rounds ×2 | Coding + System Design | Fundamentals, not AI-specific |
| Onsite | Coding / SysDesign / Deep Dive / HM | Friendly but probing |
| Decision | Holistic-signal evaluation | Opaque feedback |
From the first tech round to the final result the cycle runs about four to five weeks. Almost every debrief mentions the same thing: feedback is opaque. You often can't tell which round went wrong, and people get rejected even when the overall experience felt good.
2. Coding: Not About Algorithm Tricks, But Engineering Modeling
OpenAI coding questions rarely chase fancy algorithm tricks; they lean toward engineering-style problem modeling.
1. Infection-spread problems (the most typical class)
These usually center on a 2D grid: given initial infection sources, spread by rules. The base solution is multi-source BFS, but the real difficulty comes from later extension rules—immune cells, infection thresholds, recovery mechanics, even multi-phase state changes.
from collections import deque
def spread(grid, sources, immune):
R, C = len(grid), len(grid[0])
state = [[0] * C for _ in range(R)] # 0 healthy 1 infected 2 immune
q = deque()
for r, c in immune:
state[r][c] = 2
for r, c in sources:
if state[r][c] != 2:
state[r][c] = 1
q.append((r, c))
step = 0
# Synchronous advance: each step spreads based only on the PREVIOUS state,
# avoiding same-frame contamination
while q:
for _ in range(len(q)):
r, c = q.popleft()
for dr, dc in ((1, 0), (-1, 0), (0, 1), (0, -1)):
nr, nc = r + dr, c + dc
if 0 <= nr < R and 0 <= nc < C and state[nr][nc] == 0:
state[nr][nc] = 1 # immune cells (state==2) block automatically
q.append((nr, nc))
step += 1
return state, step
The thing being tested isn't BFS itself but how you handle synchronous updates, design the state machine, and cover edge cases. Where people get stuck is the time semantics (this frame vs. next frame) or state-transition details, not the core algorithm.
2. Structure design: toy language / type inference
Another common class is a toy language or type inference. The core is building an AST, handling generic binding, and recursive structural matching. It does NOT test parsing—you operate directly on object structures, more like writing a tiny type system.
def unify(a, b, env):
# a, b shaped like ("var","T") / ("int",) / ("list", elem) / ("fn", arg, ret)
a, b = resolve(a, env), resolve(b, env)
if a[0] == "var":
env[a[1]] = b; return True
if b[0] == "var":
env[b[1]] = a; return True
if a[0] != b[0]:
return False # type conflict
# if same shape, recursively unify each substructure
return all(unify(x, y, env) for x, y in zip(a[1:], b[1:]))
The difficulty is logical rigor: mishandle binding or conflict detection and hidden bugs appear fast. Low line count, high demand on clarity of thought.
3. Engineering-implementation problems
There are also many close-to-real-system problems: iterators, memory allocators, KV stores, time-series systems. The shared trait is being closer to a real system than a pure algorithm—you must consider state management, interface design, and code structure.
3. System Design: Classic Prompts, But It Digs Into Details
System design isn't limited to AI. The prompts range widely: chat systems, URL shorteners, payment systems, calendars, even online games. The prompts are common, but the style has a clear trait—it digs into details.
It doesn't end at a high-level diagram; it keeps asking how specific components are implemented, where the bottlenecks are, and how you trade off under different constraints. If you only practice templated system-design answers, this round trips you up. It cares whether you truly understand how the system works, not whether you memorized a playbook.
4. ML-Related Probing for Some Roles
For research/ML-leaning roles, expect ML-related coding or debugging: implement a simple layer in NumPy, analyze data, debug existing code. The focus is understanding, not memorization—explain model behavior and locate root causes rather than just calling a framework.
5. Experience and Final Decision
Most rate the process itself positively: interviewers are friendly, some even discuss the problem with you and give feedback mid-round. But the outcome is less consistent: many note that even when every round felt fine, they still got rejected, with low transparency on what went wrong.
A reasonable read: the final decision rests on holistic signal, not a single round. If any part isn't strong enough, even without an obvious fail it can sink the result.
6. Summary
- Coding: heavy on engineering modeling, light on algorithm tricks; infection-spread BFS and type inference are the two frequent classes.
- System Design: classic prompts + relentless detail-digging, no templates.
- Decision: holistic signal, opaque feedback—keep your composure.
FAQ
Q1: Do I need dedicated AI/ML prep for OpenAI?
The coding/system-design rounds in the tech screen and onsite test fundamentals (data structures, algorithms, general system design), not AI specifically. Only research/ML-leaning roles bring NumPy-layer implementation or model debugging.
Q2: What makes OpenAI coding hard?
Not the algorithm tricks but the engineering modeling. The infection-spread trap is synchronous-update time semantics and state-machine design; the type-inference trap is logical rigor in binding/conflict detection.
Q3: How do I prep system design?
Don't just memorize templates. OpenAI probes from high-level architecture down to component implementation, bottlenecks, and trade-offs. Pick 2-3 classic prompts (chat / shortener / payments) and push each module's design rationale deep.
Q4: Is it normal to feel good every round and still get rejected?
Very common. OpenAI's feedback is opaque and the decision is on holistic signal—a strong single round doesn't guarantee a pass. Steady your cadence and run timed mocks on weak spots.
Preparing for OpenAI?
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