OpenAI Research Engineer Interview Guide

The short answer: OpenAI publishes one interview guide for every role: a resume review, introductory calls, a skills-based assessment whose format varies by team, then final interviews of 4 to 6 hours with 4 to 6 interviewers. OpenAI says its engineering interviews look for well-designed solutions, high-quality code, good performance and test coverage. Applying to its general Research Engineer posting puts you in consideration for research engineer roles on every team. Candidate reports describe two 60-minute technical screens, one general coding and one ML coding in NumPy and PyTorch, and final rounds that include debugging transformer code. Last reviewed October 2026.

OpenAI research engineer interview: quick answers

The OpenAI research engineer role

What OpenAI research engineers work on, the skills its job postings ask for, and what a week can look like. OpenAI posts the role as Research Engineer (one general posting plus team postings such as Codex and AI for Chip Design), and combined Research Engineer / Research Scientist postings for each team.

OpenAI research engineer levels and pay

Your level sets the bar you are interviewed against and the pay band of the offer.

OpenAI does not publish level names in its postings, and its job board showed no Member of Technical Staff title in October 2026. The pay table uses posting titles.

Pay in OpenAI's US job postings

OpenAI's US postings for this role are all in San Francisco and list an annual base salary range. Most also list an equity component.

OpenAI posts no levels, so each range belongs to one posting. The Research Engineer, Privacy posting shows a range starting at $0 and is left out. Ranges seen October 29, 2026.

OpenAI research engineer interview process

OpenAI publishes one interview guide for all roles, so the steps below follow it. What happens inside the technical screens and final rounds comes from candidate reports, and OpenAI says individual experiences may differ from its example.

Based on OpenAI's own pages and candidate reports.

OpenAI says each stage usually turns around in about a week and that it aims for a consistent process, though experiences differ. One candidate report describes about 7 weeks from the recruiter screen to a decision. Last reviewed October 2026.

What is different at OpenAI

• One application covers every team. OpenAI says applying to its general Research Engineer posting puts you in consideration for research engineer roles across all of its teams. Team postings also exist if you want a specific group.
• The line between engineer and scientist is thin. Most research openings are combined Research Engineer / Research Scientist postings for one team. The separate Research Scientist posting centers on owning a research agenda and first-author publications.
• The role is engineering-heavy. The general posting asks for large distributed ML systems and bug-free ML code, and says OpenAI expects engineering to drive most future AI progress. Candidate guides describe the loop as more coding-focused than research-focused.
• Teams hiring research engineers include RL and Reasoning (the o1 and o3 work), post-training teams (Personal AGI, Health, Human Flourishing), Foundations (Search, Knowledge Graphs), Codex Research, AI for Chip Design, RSI and Security (Privacy).
• AI tools depend on the interview. OpenAI says some formats intentionally allow AI and others are built to test your own problem-solving, and that your prep materials say which applies.
• The work is hybrid in San Francisco. Team postings say 3 days a week in the office, with relocation help. We found no on-call requirement in research engineer postings.

OpenAI research engineer interview questions

Questions from our bank, then practice prompts we wrote for this guide around OpenAI's work.

1from typing import Dict, List, Tuple
2
3def train_bpe(word_counts: Dict[str, int], num_merges: int) -> List[Tuple[str, str]]:
4    """Return the BPE merges learned from word_counts, in order."""
5    pass

1from collections import Counter
2from typing import Dict, List, Tuple
3
4def train_bpe(word_counts: Dict[str, int], num_merges: int) -> List[Tuple[str, str]]:
5    words = {tuple(w): c for w, c in word_counts.items() if w}
6    merges = []
7    for _ in range(num_merges):
8        pairs = Counter()
9        for symbols, count in words.items():
10            for pair in zip(symbols, symbols[1:]):
11                pairs[pair] += count
12        if not pairs:
13            break
14        best = min(pairs, key=lambda p: (-pairs[p], p))  # most frequent, then smallest
15        merges.append(best)
16        joined = best[0] + best[1]
17        new_words = Counter()
18        for symbols, count in words.items():
19            out, i = [], 0
20            while i < len(symbols):
21                if i + 1 < len(symbols) and (symbols[i], symbols[i + 1]) == best:
22                    out.append(joined)
23                    i += 2
24                else:
25                    out.append(symbols[i])
26                    i += 1
27            new_words[tuple(out)] += count
28        words = new_words
29    return merges
30
31# Complexity: O(num_merges x total symbols) time, O(total symbols) space.

How to prepare

A plan for the OpenAI loop, most important first:

1. Practice ML coding in NumPy and PyTorch: attention, KV caching, a training loop and vectorized operations, written and debugged from scratch.
2. Practice debugging transformer code you did not write, starting from a symptom such as a loss stuck at ln(vocab size).
3. Practice multi-part coding problems using only the standard library, with tests, in two 60-minute sessions back to back.
4. Read OpenAI's recent research and blog posts for your target team, and prepare questions and a point of view on them.
5. Review distributed training basics: data, tensor and pipeline parallelism, collectives, memory per parameter and compute estimates.
6. Prepare a deep dive on one project with the hypothesis, the experiments, what failed and what you would test next.

Practice path

Courses that cover the rounds above:

• LLM Architecture
• PyTorch
• Deep Learning
• GPU Programming
• ML System Design