OpenAI AI Engineer Interview Guide

From hundreds of mock interviews, we see the same pattern: strong engineers prep for OpenAI like it's another big-tech loop and get blindsided when the interview probes production systems thinking, LLM internals like RLHF and inference optimization, and your ability to narrate technical tradeoffs under pressure. The role itself is heavily applied, not "train models all day," and the interview is calibrated to match.

OpenAI AI Engineer Role

The AI Engineer title at OpenAI maps closer to "forward-deployed AI engineer" than to a traditional ML engineer. You're building the systems behind ChatGPT's retrieval features, prototyping agent orchestration loops for the API platform, and shipping working demos internally. Success after year one looks like owning a production system that real users touch, whether that's a RAG pipeline serving ChatGPT's browsing capability or an eval harness the agents team relies on daily.

A Typical Week

The thing that catches most candidates off guard is how little time goes to pure ML modeling. The bulk of your week is coding, infrastructure, writing, and meetings, with actual analysis and eval work occupying a surprisingly thin slice. Tuesday you're deep in Python prototyping (debugging context window overflows in agent traces, reviewing RAG chunking PRs), and by Thursday you're live-demoing that prototype to people from the agents and safety teams.

Projects & Impact Areas

RAG pipeline work for ChatGPT's browsing and retrieval features is the bread and butter: investigating embedding drift after a docs refresh, rewriting chunking logic, re-indexing a knowledge base mid-week. You might also be building agent orchestration frameworks that chain code interpreter and retrieval tools with structured output parsing, directly feeding into the API products that enterprise customers pay for. Some AI Engineers work on internal tooling for eval and safety, while others build custom solutions for OpenAI's growing enterprise push.

Skills & What's Expected

The most underrated skill for this role is software engineering discipline. Candidates fixate on ML theory and LLM knowledge (both required at expert level), but the engineers who fail here usually write sloppy, notebook-quality Python rather than production code with type hints and edge case handling. Don't ignore the communication dimension either: the behavioral and panel rounds test whether you can connect a chunking strategy change to user-facing retrieval quality, or explain why you chose one embedding model over another in terms of latency and cost.

Levels & Career Growth

The widget shows the full L3 through L7 ladder. The single biggest promotion blocker at mid-levels is scope: you can write perfect code all day, but if you're not independently leading projects and defining technical direction, you'll stall. OpenAI's relatively small engineering org means the impact-per-engineer ratio is high, which can accelerate visibility if you ship work that matters.

Work Culture

OpenAI's day-in-life data points to three days a week in the San Francisco office (most teams cluster Tuesday through Thursday), with many engineers coming in more often because hallway conversations drive real decisions. The pace is intense, and the culture explicitly rewards shipping speed over polish. Be eyes-open about the organizational turbulence (the Altman board saga, the nonprofit-to-for-profit conversion controversy, departures of safety-focused staff), but also know that immediate equity vesting and competitive compensation make it harder to walk away.

OpenAI AI Engineer Compensation

OpenAI removed its equity vesting cliff in December 2025, so your shares start accruing from day one. That's not just a perk. It means if you leave at month six, you keep six months of vested equity rather than walking away empty-handed. But OpenAI equity is private, not publicly traded, so the gap between your on-paper total comp and actual cash-in-hand can be significant.

OpenAI is in a direct bidding war with Anthropic, Google DeepMind, and Meta FAIR for the same candidates, and recruiters know it. A competing offer from any of those three gives you real leverage on equity size, which is where the biggest dollar swings happen. If you're targeting L5 or above, negotiate the level itself by pointing to specific production AI systems you've owned (say, a RAG pipeline serving millions of queries or an inference optimization that cut serving costs), because at OpenAI's scale, one level jump can mean hundreds of thousands in additional equity.

OpenAI AI Engineer Interview Process

The Presentation round is the one that catches people off guard. You're pitching a past project to a panel that includes engineers who ship ChatGPT and Codex features daily, and they'll press you on why you chose your retrieval strategy over alternatives, how you measured success, and what broke in production. Preparing a polished deck isn't enough. You need to rehearse fielding hostile follow-ups about your own design decisions until the answers feel effortless.

Consistency across every round matters more than brilliance in a few. The common rejection reasons in this process cluster around gaps that surface repeatedly: hand-wavy system designs missing SLOs and failure modes, ML debugging answers that never get past "I retrained the model," or project stories that end at a notebook instead of a deployed service with monitoring and rollback. Because OpenAI's loop covers coding, system design, ML depth, presentation, and behavioral separately, a weak spot in any one dimension gets isolated and recorded. You can't offset a shallow ML round with a stellar coding performance.

OpenAI AI Engineer Interview Questions

from __future__ import annotations

from collections import defaultdict
from typing import Dict, List


def longest_window_at_most_k_types(events: List[Dict], k: int) -> int:
    """Return the maximum inclusive timestamp span with at most k distinct event types.

    Each event is a dict: {"ts": int, "request_id": str, "type": str}

    The window is defined over timestamps after sorting by ts.
    If multiple events share the same timestamp, they are treated as separate items,
    and the window span uses ts values: span = events[r].ts - events[l].ts + 1.

    Time: O(n log n) due to sorting. Space: O(k).
    """
    if k < 0:
        raise ValueError("k must be >= 0")
    if not events or k == 0:
        return 0

    # Sort by timestamp. Stable sort keeps relative order for ties, not required.
    ev = sorted(events, key=lambda e: e["ts"])

    freq = defaultdict(int)  # type: Dict[str, int]
    distinct = 0
    best = 0
    l = 0

    for r in range(len(ev)):
        t = ev[r]["type"]
        if freq[t] == 0:
            distinct += 1
        freq[t] += 1

        # Shrink until the constraint is satisfied.
        while distinct > k:
            lt = ev[l]["type"]
            freq[lt] -= 1
            if freq[lt] == 0:
                distinct -= 1
            l += 1

        # Window [l, r] is valid.
        span = ev[r]["ts"] - ev[l]["ts"] + 1
        if span > best:
            best = span

    return best


if __name__ == "__main__":
    # Basic sanity checks
    sample = [
        {"ts": 5, "request_id": "a", "type": "tool_call"},
        {"ts": 2, "request_id": "b", "type": "tool_result"},
        {"ts": 3, "request_id": "c", "type": "tool_call"},
        {"ts": 3, "request_id": "d", "type": "token"},
        {"ts": 10, "request_id": "e", "type": "token"},
    ]
    assert longest_window_at_most_k_types(sample, 2) == 4  # ts 2..5
    assert longest_window_at_most_k_types(sample, 1) == 1  # best single-type span
    assert longest_window_at_most_k_types(sample, 0) == 0

/* Daily thumbs-up rate for assistant messages, using latest feedback per (message_id, user_id).
   Assumptions:
   - rating is either 'up' or 'down' (or boolean-like).
   - You want rate over assistant messages that have at least one feedback row.
*/
WITH assistant_messages AS (
  SELECT
    m.message_id,
    m.created_at::date AS message_date
  FROM chat_messages AS m
  WHERE m.role = 'assistant'
),
latest_feedback AS (
  SELECT
    f.message_id,
    f.user_id,
    f.rating,
    f.created_at,
    ROW_NUMBER() OVER (
      PARTITION BY f.message_id, f.user_id
      ORDER BY f.created_at DESC
    ) AS rn
  FROM message_feedback AS f
),
deduped_feedback AS (
  SELECT
    lf.message_id,
    lf.user_id,
    lf.rating
  FROM latest_feedback AS lf
  WHERE lf.rn = 1
)
SELECT
  am.message_date AS day,
  COUNT(*) AS feedback_count,
  SUM(CASE WHEN df.rating IN ('up', 'thumbs_up', 'positive', '1', 'true') THEN 1 ELSE 0 END) AS thumbs_up_count,
  (SUM(CASE WHEN df.rating IN ('up', 'thumbs_up', 'positive', '1', 'true') THEN 1 ELSE 0 END)::numeric
    / NULLIF(COUNT(*), 0)) AS thumbs_up_rate
FROM assistant_messages AS am
JOIN deduped_feedback AS df
  ON df.message_id = am.message_id
GROUP BY 1
ORDER BY 1;

The distribution skews hard toward applied LLM work and production system design, which mirrors how OpenAI's forward-deployed engineers actually spend their time: building RAG pipelines for enterprise customers, wiring up Agents API workflows, and shipping under real latency and safety constraints. Where this gets brutal is that those two areas compound on each other. You can't design a serving architecture for an agent that writes to a customer's Postgres without also reasoning about function-calling reliability, guardrails, and rollout safety, so prepping these domains in isolation leaves you unprepared for the questions that actually decide the outcome.

Practice questions across all seven areas at datainterview.com/questions.

How to Prepare for OpenAI AI Engineer Interviews

OpenAI is pushing simultaneously on three product bets: turning ChatGPT from a reactive chatbot into a proactive assistant, launching Codex as a cloud-native coding agent, and shipping its first hardware device in 2026. For AI Engineers, that translates to building retrieval and serving systems for products like Atlas one sprint, then tackling latency and footprint constraints for a hardware form factor the next. Every project ties back to ChatGPT's installed base of hundreds of millions of users, which means your systems work ships to production at a scale most AI startups never touch.

Most candidates blow the "why OpenAI" question by talking about AGI ambitions. Interviewers are tired of it. What separates you is showing you've grappled with the tension between OpenAI's original charter and its reported shift toward shipping velocity (Semafor reported on how the company's core values quietly changed).

Articulate where you personally draw the line between moving fast and being careful, and ground it in a specific product decision. That kind of specificity signals real homework.

Try a Real Interview Question

from typing import List, Tuple


def pack_context(passages: List[Tuple[int, float]], budget: int) -> List[int]:
    """Select passage indices maximizing total score under a token budget.

    Args:
        passages: List of (tokens, score) pairs.
        budget: Maximum total tokens allowed.

    Returns:
        Indices of selected passages in increasing order.
    """
    pass

from __future__ import annotations

from typing import List, Tuple, Optional


def pack_context(passages: List[Tuple[int, float]], budget: int) -> List[int]:
    """Select passage indices maximizing total score under a token budget.

    Uses 0/1 knapsack DP with deterministic tie-breaking.

    Tie-breaking (in order):
      1) Higher total score
      2) Fewer passages
      3) Lexicographically smaller index list

    Returns indices in increasing order.
    """

    if budget <= 0 or not passages:
        return []

    n = len(passages)

    # dp[b] holds a tuple (score, count, indices_tuple) for best solution at budget b.
    # Initialize with empty selection.
    dp_score: List[float] = [0.0] * (budget + 1)
    dp_count: List[int] = [0] * (budget + 1)
    dp_idxs: List[Tuple[int, ...]] = [tuple() for _ in range(budget + 1)]

    def better(a_score: float, a_count: int, a_idxs: Tuple[int, ...],
               b_score: float, b_count: int, b_idxs: Tuple[int, ...]) -> bool:
        """Return True if A is better than B under the tie-break rules."""
        # Higher score wins.
        if a_score != b_score:
            return a_score > b_score
        # Fewer passages wins.
        if a_count != b_count:
            return a_count < b_count
        # Lexicographically smaller index list wins.
        return a_idxs < b_idxs

    for i, (tokens, score) in enumerate(passages):
        if tokens <= 0:
            # Non-positive token lengths are invalid for packing, skip.
            continue
        if tokens > budget:
            continue
        # Iterate budgets descending to enforce 0/1 selection.
        for b in range(budget, tokens - 1, -1):
            prev_b = b - tokens
            cand_score = dp_score[prev_b] + float(score)
            cand_count = dp_count[prev_b] + 1
            cand_idxs = dp_idxs[prev_b] + (i,)

            cur_score = dp_score[b]
            cur_count = dp_count[b]
            cur_idxs = dp_idxs[b]

            if better(cand_score, cand_count, cand_idxs, cur_score, cur_count, cur_idxs):
                dp_score[b] = cand_score
                dp_count[b] = cand_count
                dp_idxs[b] = cand_idxs

    # Choose best across all budgets <= budget.
    best_score = 0.0
    best_count = 0
    best_idxs: Tuple[int, ...] = tuple()
    for b in range(budget + 1):
        if better(dp_score[b], dp_count[b], dp_idxs[b], best_score, best_count, best_idxs):
            best_score = dp_score[b]
            best_count = dp_count[b]
            best_idxs = dp_idxs[b]

    return list(best_idxs)

OpenAI runs two separate coding rounds, with the second being harder and more open-ended than the first. Per their own interview guide, they care about how you think through problems, not just whether you reach the answer. Sharpen your Python algorithm skills at datainterview.com/coding, paying special attention to problems where a naive solution times out and you need to reason about a tighter approach out loud.

Test Your Readiness

Find out which topic areas will cost you before the real thing at datainterview.com/questions. Spend the bulk of your remaining prep on whatever surprises you most.