Section 1: How OpenAI Evaluates Machine Learning Engineers in 2026
OpenAI’s machine learning interviews are fundamentally different from most FAANG-style ML interviews. While other companies evaluate ML engineers primarily on ranking systems, experimentation velocity, or enterprise reliability, OpenAI evaluates candidates on a deeper question:
Can you safely and rigorously build models that reason, generalize, and scale toward increasingly capable AI systems?
By 2026, OpenAI’s hiring philosophy has converged around five defining dimensions: research-to-production fluency, reasoning about model behavior, safety and alignment awareness, infrastructure scale judgment, and epistemic humility. Candidates who treat the interview as a conventional ML system design loop often struggle, not because they lack skill, but because they misunderstand what OpenAI is optimizing for.
The first critical thing to understand is that OpenAI does not separate “research ML” and “production ML” as cleanly as many companies. Even roles labeled as “engineer” are expected to reason about model behavior, failure modes, generalization, and alignment, not just pipelines and metrics. Interviewers therefore probe whether candidates can move fluidly between theory, experimentation, and deployment.
This is where many strong candidates falter. They answer OpenAI questions as if they were interviewing for a recommender system or an ad-ranking team, optimizing metrics, tuning architectures, or discussing latency tradeoffs, without addressing why a model behaves the way it does, how it might fail in unseen regimes, or how its incentives are shaped during training.
At OpenAI, a model that performs well but behaves unpredictably is considered dangerous, not impressive.
A defining characteristic of OpenAI’s ML interviews is their focus on reasoning about models, not just optimizing them. Interviewers often ask questions that appear deceptively simple, about loss functions, scaling laws, fine-tuning, or evaluation, but are listening for whether candidates can reason about second-order effects, generalization limits, and emergent behaviors.
This is especially true for large language models and multimodal systems. OpenAI interviewers expect candidates to understand that LLMs are not just “bigger neural networks,” but systems with qualitatively different behaviors as scale increases. Candidates who discuss LLMs purely in terms of architecture without addressing reasoning, alignment, or brittleness often underperform.
Another major axis of evaluation is safety and alignment thinking. Unlike most companies, OpenAI treats safety as a core technical constraint, not a policy overlay. Interviewers expect candidates to think about misuse, hallucination, reward hacking, distribution shift, and adversarial behavior as engineering problems.
This emphasis aligns with broader industry trends where responsible AI is becoming a hiring signal, as discussed in The New Rules of AI Hiring: How Companies Screen for Responsible ML Practices. At OpenAI, however, these concerns are not peripheral, they are central to whether a candidate is trusted.
OpenAI also evaluates candidates on epistemic humility, the ability to reason under uncertainty, admit unknowns, and avoid overclaiming. Interviewers often probe how candidates respond when models behave unexpectedly, metrics conflict, or assumptions break. Candidates who insist on confident but brittle explanations are often scored lower than those who reason carefully and revise beliefs.
Infrastructure and scale still matter, but they are evaluated through a different lens. OpenAI operates some of the largest training and inference workloads in the world, but interviewers are less interested in whether you know specific tools and more interested in how you reason about scaling decisions, failure modes, and tradeoffs. They want to see whether you understand when scale helps, and when it introduces new risks.
Another subtle but important difference is how OpenAI evaluates impact. Unlike ad-driven companies, OpenAI’s success metrics are not always directly tied to revenue or engagement. Interviewers therefore listen for whether candidates can reason about model quality, usefulness, and safety even when metrics are imperfect or delayed.
This stands in contrast to metric-centric ML interviews at companies like Amazon or Meta, and closer to themes discussed in The Hidden Metrics: How Interviewers Evaluate ML Thinking, Not Just Code , where reasoning quality outweighs surface-level correctness.
Finally, OpenAI evaluates seniority differently than most companies. Senior ML engineers and researchers are not defined by the size of their models or the number of papers they’ve shipped. They are defined by their ability to anticipate failure modes, guide safe scaling, and make conservative decisions under uncertainty.
The goal of this guide is to help you prepare with that reality in mind. Each section that follows will break down real OpenAI-style ML interview questions, explain why OpenAI asks them, show how strong candidates reason through them, and highlight the deeper signals interviewers are listening for.
If you approach OpenAI ML interviews like standard FAANG ML interviews, they will feel abstract and ambiguous. If you approach them as conversations about understanding, scaling, and safely deploying highly capable models, they become structured and navigable.
Section 2: Core ML Fundamentals, Losses & Model Behavior at OpenAI (Questions 1–5)
At OpenAI, “ML fundamentals” are not assessed as isolated algorithms or formulas. Interviewers use these questions to evaluate whether you understand how training objectives shape model behavior, where those objectives fail, and how choices made during training propagate into emergent capabilities and risks. Candidates who answer at a purely mechanical level often miss what OpenAI is actually testing.
1. How does the choice of loss function influence model behavior in large language models?
Why OpenAI asks this
Loss functions are not neutral. This question tests whether you understand that optimization defines incentives, and incentives define behavior.
How strong candidates answer
Strong candidates explain that common losses (e.g., cross-entropy) optimize for next-token likelihood, which implicitly rewards surface-level plausibility rather than truth, reasoning, or alignment. They discuss how this leads to behaviors like hallucination or overconfidence, especially out of distribution.
They also note that auxiliary objectives, fine-tuning stages, or post-training alignment methods exist precisely because the base loss is insufficient.
Example
A model trained purely on likelihood may generate fluent but incorrect explanations when unsure.
What interviewers listen for
Whether you treat the loss as a behavioral contract, not a math detail.
2. Why can a model achieve low training loss but still behave poorly in real-world usage?
Why OpenAI asks this
OpenAI routinely sees models that “optimize well” yet fail in deployment. This question tests generalization reasoning.
How strong candidates answer
Strong candidates explain that low loss reflects performance on the training distribution, not robustness or alignment in novel contexts. They discuss dataset bias, spurious correlations, and the gap between proxy objectives and real-world goals.
They also mention that as models scale, they may exploit shortcuts in the data that minimize loss but do not reflect true understanding.
Example
A language model trained on web text may confidently answer questions that have no factual grounding.
What interviewers listen for
Whether you distinguish optimization success from deployment success.
3. How do you think about overfitting and memorization in large models?
Why OpenAI asks this
Large models blur the line between memorization and generalization. This question tests nuanced understanding, not dogma.
How strong candidates answer
Strong candidates explain that memorization is not binary. Models may memorize rare patterns while generalizing common ones. They discuss factors like data duplication, model capacity, and regularization, but also emphasize evaluation strategies to detect harmful memorization.
They avoid simplistic claims that “bigger models don’t overfit” and instead focus on risk management.
Example
A model may memorize sensitive strings present only a few times in the training corpus.
What interviewers listen for
Whether you reason about risk and mitigation, not absolutes.
4. Why do large models exhibit emergent behaviors as they scale?
Why OpenAI asks this
Emergence is central to OpenAI’s work. This question tests whether you can reason beyond linear intuitions.
How strong candidates answer
Strong candidates explain that as model capacity and data scale increase, representations become richer, enabling behaviors not explicitly trained for. They discuss phase-transition-like effects, where small increases in scale unlock qualitatively new capabilities.
They also acknowledge that emergence complicates predictability and safety.
Example
A model suddenly demonstrating multi-step reasoning once it crosses a scale threshold.
What interviewers listen for
Whether you recognize scaling as a source of uncertainty, not just power.
5. How do you evaluate model behavior when traditional metrics are insufficient?
Why OpenAI asks this
OpenAI frequently operates where ground truth is ambiguous. This question tests evaluation creativity and rigor.
How strong candidates answer
Strong candidates explain that evaluation must combine quantitative metrics with qualitative analysis. They discuss targeted evaluations, red-teaming, behavioral probes, and scenario-based testing to surface failure modes.
They also emphasize iterating on evaluation itself as models evolve.
This mindset aligns with OpenAI’s approach to model assessment and differs from purely metric-driven ML organizations, reinforcing why reasoning quality matters more than numerical optimization.
Example
Using adversarial prompts to test robustness rather than relying solely on benchmark scores.
What interviewers listen for
Whether you treat evaluation as a living process, not a checklist.
Why This Section Matters
OpenAI interviewers use these questions to determine whether candidates understand that training choices shape behavior in profound and sometimes dangerous ways. Candidates who treat ML fundamentals as static theory often miss the point. Candidates who reason about incentives, emergence, and failure modes demonstrate readiness to work on highly capable models.
This section often determines whether interviewers trust you to reason about models beyond what the metrics say.
Section 3: Training, Fine-Tuning & Scaling Tradeoffs at OpenAI (Questions 6–10)
At OpenAI, training is not viewed as a linear pipeline where more data and more compute simply produce better models. Interviewers use this section to assess whether candidates understand why training choices matter, how scaling introduces new failure modes, and how fine-tuning reshapes model incentives. Candidates who describe training as a mechanical optimization process often miss what OpenAI is actually testing.
6. How do you decide when to scale model size versus improving data quality?
Why OpenAI asks this
OpenAI operates at the frontier where both compute and data are expensive. This question tests judgment under resource constraints.
How strong candidates answer
Strong candidates explain that scaling model size improves capacity, but poor data limits what that capacity can learn. They discuss evaluating marginal returns: whether additional parameters reduce loss meaningfully, or whether noise and bias in the data dominate.
They emphasize that data curation, filtering, and diversity often yield higher returns than brute-force scaling, especially for alignment and robustness.
This reasoning mirrors broader ML decision-making where understanding tradeoffs matters more than raw scale, similar to themes discussed in Beyond the Model: How to Talk About Business Impact in ML Interviews.
Example
Improving data quality to reduce hallucinations may outperform increasing model size alone.
What interviewers listen for
Whether you reason in terms of diminishing returns, not maximal scale.
7. What are the risks introduced by aggressive scaling of large language models?
Why OpenAI asks this
Scaling unlocks power, but also risk. This question tests anticipation of failure modes.
How strong candidates answer
Strong candidates explain that aggressive scaling can amplify hallucinations, bias, misuse potential, and unpredictability. They discuss emergent behaviors that are hard to foresee and the challenge of evaluating models that surpass existing benchmarks.
They also mention infrastructure risks, such as training instability and debugging difficulty.
Example
A scaled model exhibiting new reasoning capabilities but also more confident misinformation.
What interviewers listen for
Whether you view scaling as risk amplification, not just capability growth.
8. How does fine-tuning change a model’s behavior compared to pretraining?
Why OpenAI asks this
Fine-tuning is where alignment and usability are shaped. This question tests incentive understanding.
How strong candidates answer
Strong candidates explain that pretraining shapes broad knowledge and representations, while fine-tuning sharpens behavior toward specific objectives, helpfulness, harmlessness, or task performance. They emphasize that fine-tuning can introduce new biases or overfitting to narrow objectives if not carefully designed.
They also discuss tradeoffs between generality and specialization.
This nuanced view aligns with OpenAI’s emphasis on understanding how training stages interact, rather than treating them independently.
Example
An instruction-tuned model becoming more verbose or cautious due to alignment objectives.
What interviewers listen for
Whether you see fine-tuning as behavioral reshaping, not mere improvement.
9. How do you evaluate whether fine-tuning has improved or harmed a model?
Why OpenAI asks this
Fine-tuning can introduce subtle regressions. This question tests evaluation discipline.
How strong candidates answer
Strong candidates explain that evaluation must go beyond aggregate metrics. They discuss targeted behavioral tests, regression suites, and red-teaming to detect new failure modes.
They also emphasize comparing behavior across distributions, not just headline benchmarks.
Example
Detecting that an aligned model is less willing to answer benign but sensitive questions.
What interviewers listen for
Whether you anticipate regressions, not just gains.
10. How do you decide when a model is “ready” to be deployed or shared?
Why OpenAI asks this
Deployment decisions at OpenAI carry global impact. This question tests deployment judgment under uncertainty.
How strong candidates answer
Strong candidates explain that readiness is not defined by performance alone. They discuss safety evaluations, misuse risk assessment, and staged release strategies.
They emphasize that uncertainty should slow deployment, not accelerate it, and that rollback and monitoring plans are essential.
Example
Delaying release of a capable model until safety mitigations and monitoring are in place.
What interviewers listen for
Whether you prioritize responsible deployment over speed.
Why This Section Matters
OpenAI interviewers use these questions to identify candidates who understand that training and scaling decisions reshape model behavior in unpredictable ways. Candidates who treat training as a mechanical process often underperform. Candidates who reason about incentives, risk, and evaluation demonstrate readiness to work on frontier models.
This section often determines whether interviewers trust you to participate in decisions that shape how powerful models are trained and released.
Section 4: Evaluation, Safety & Alignment in Practice at OpenAI (Questions 11–15)
At OpenAI, evaluation and safety are not downstream concerns, they are core engineering disciplines. Interviewers use this section to assess whether candidates can reason about model behavior under uncertainty, detect subtle failure modes, and design evaluation systems that evolve alongside increasingly capable models. Candidates who rely exclusively on benchmarks or static metrics often struggle here.
11. How do you evaluate model quality when benchmarks are saturated or misleading?
Why OpenAI asks this
Frontier models quickly saturate standard benchmarks. This question tests evaluation creativity and rigor.
How strong candidates answer
Strong candidates explain that when benchmarks lose discriminative power, evaluation must shift toward targeted behavioral tests. They discuss constructing task suites that probe reasoning depth, robustness to adversarial prompts, and performance under distribution shift.
They also emphasize qualitative analysis, carefully inspecting failure cases to understand why a model behaves a certain way.
Example
Designing custom reasoning probes instead of relying on aggregate benchmark scores.
What interviewers listen for
Whether you treat evaluation as adaptive, not fixed.
12. How do you think about alignment failures versus capability failures?
Why OpenAI asks this
Not all failures are equal. This question tests conceptual clarity about risk.
How strong candidates answer
Strong candidates distinguish between capability failures (the model cannot do something) and alignment failures (the model does the wrong thing). They explain that alignment failures are often more dangerous because they can scale with capability.
They also discuss that improving capability without alignment can increase harm, reinforcing the need to co-develop both.
Example
A model that confidently provides harmful advice is an alignment failure, not a capability gap.
What interviewers listen for
Whether you prioritize alignment risk as capability grows.
13. How do you detect and mitigate hallucinations in large language models?
Why OpenAI asks this
Hallucination is one of the most visible LLM failure modes. This question tests practical mitigation thinking.
How strong candidates answer
Strong candidates explain that hallucinations arise from likelihood-based training objectives. They discuss mitigation strategies such as improved data curation, uncertainty-aware prompting, retrieval augmentation, and fine-tuning with refusal or uncertainty signals.
They also emphasize evaluation, measuring hallucination rates in realistic scenarios rather than contrived tests.
This grounded approach reflects OpenAI’s emphasis on understanding model behavior, not just suppressing symptoms.
Example
Using retrieval to ground responses and prompting models to acknowledge uncertainty.
What interviewers listen for
Whether you frame hallucination as incentive-driven, not accidental.
14. How do you design red-teaming or adversarial evaluations for OpenAI models?
Why OpenAI asks this
Red-teaming is central to OpenAI’s safety process. This question tests adversarial thinking.
How strong candidates answer
Strong candidates explain that red-teaming involves actively searching for failure modes, misuse, jailbreaks, bias amplification, using both automated tools and human experts. They emphasize iterative feedback loops where findings directly inform training and mitigation.
They also acknowledge that red-teaming is never complete; it evolves as models and users adapt.
Example
Simulating malicious users to probe safety boundaries before deployment.
What interviewers listen for
Whether you see red-teaming as continuous, not a one-off exercise.
15. How do you balance model usefulness with safety constraints?
Why OpenAI asks this
Over-restrictive models lose value; under-restrictive models cause harm. This question tests tradeoff judgment.
How strong candidates answer
Strong candidates explain that safety constraints should be proportional to risk. They discuss graduated responses, refusal, safe completion, or redirection, rather than blanket blocking.
They also emphasize measuring user impact and iterating based on real-world feedback.
This balance reflects OpenAI’s philosophy of deploying useful yet safe systems, rather than optimizing for one dimension alone.
Example
Allowing high-level explanations of sensitive topics while blocking actionable harm.
What interviewers listen for
Whether you demonstrate nuanced, context-aware decision-making.
Why This Section Matters
OpenAI interviewers know that the most serious failures are rarely obvious. They emerge from subtle interactions between capability, incentives, and context. Candidates who understand evaluation and safety as evolving, adversarial processes demonstrate readiness to work on models with real-world impact.
This section often determines whether interviewers trust you to reason about risk, alignment, and uncertainty, not just performance.
Section 5: Infrastructure, Deployment & Operating Frontier Models at OpenAI (Questions 16–20)
Operating frontier models is fundamentally different from running conventional ML services. At OpenAI, infrastructure choices determine not only cost and latency, but also safety, reliability, and controllability. Interviewers use this section to evaluate whether candidates can reason about deploying and operating highly capable models under uncertainty, where failures may be subtle, emergent, and globally impactful.
16. How do you design infrastructure to serve frontier models reliably at scale?
Why OpenAI asks this
OpenAI serves models that are both computationally expensive and behaviorally sensitive. This question tests systems thinking under extreme constraints.
How strong candidates answer
Strong candidates explain that reliability starts with architectural separation: decoupling model serving from downstream applications, isolating failure domains, and implementing strict resource controls. They discuss load shedding, prioritization, and graceful degradation to protect core functionality during spikes.
They also emphasize observability across the entire serving stack, latency, errors, and behavioral signals, because infrastructure issues can masquerade as model regressions.
Example
Rate-limiting lower-priority requests to preserve availability for safety-critical services.
What interviewers listen for
Whether you design for predictable failure, not perfect uptime.
17. How do you manage deployment risk when releasing new model versions?
Why OpenAI asks this
A new model version can change behavior in non-obvious ways. This question tests risk-aware deployment judgment.
How strong candidates answer
Strong candidates describe staged releases: shadow deployments, limited previews, and progressive rollouts with strict monitoring. They emphasize rollback readiness and the ability to compare behavioral deltas between versions in real time.
They also discuss release criteria that include safety and misuse signals, not just performance improvements.
This deployment discipline reflects how OpenAI treats releases as safety events, not routine updates, and aligns with system-design thinking discussed in Machine Learning System Design Interview: Crack the Code with InterviewNode.
Example
Holding back a rollout when early signals show increased hallucination in specific domains.
What interviewers listen for
Whether you prioritize behavioral stability over velocity.
18. How do you monitor deployed models for subtle or emergent failures?
Why OpenAI asks this
Frontier models can fail quietly. This question tests observability beyond metrics.
How strong candidates answer
Strong candidates explain that monitoring must include behavioral analytics: shifts in response length, confidence, refusal rates, or topic distribution. They also mention user feedback channels and targeted probes that continuously test known risk areas.
They emphasize that alerts should surface patterns, not just thresholds.
Example
Detecting a gradual increase in overly confident answers to ambiguous questions.
What interviewers listen for
Whether you monitor behavioral signals, not just system health.
19. How do you handle incidents involving model misuse or unexpected behavior?
Why OpenAI asks this
Incidents are inevitable. This question tests incident response maturity.
How strong candidates answer
Strong candidates describe a structured response: contain impact, disable or restrict affected capabilities, communicate clearly internally, and investigate root causes. They emphasize learning loops, feeding findings back into training, evaluation, and policy.
They also note the importance of transparency and documentation, especially when incidents inform future safeguards.
Example
Temporarily restricting a feature while deploying mitigations for a newly discovered jailbreak.
What interviewers listen for
Whether you demonstrate ownership without defensiveness.
20. How do you balance performance, cost, and safety when operating large models?
Why OpenAI asks this
Frontier models are expensive. This question tests multi-objective optimization under uncertainty.
How strong candidates answer
Strong candidates explain that cost optimization must not undermine safety or reliability. They discuss strategies like adaptive routing, model tiering, and selective capability activation to manage cost while preserving safeguards.
They also emphasize that performance gains are not worth pursuing if they degrade controllability or evaluation confidence.
This tradeoff-oriented reasoning aligns with OpenAI’s broader approach to deploying useful but safe systems, where restraint is often a strength.
Example
Routing high-risk queries to more conservative model configurations.
What interviewers listen for
Whether you balance capability, cost, and control deliberately.
Why This Section Matters
OpenAI interviewers know that the hardest problems arise after models are trained. Candidates who can reason about infrastructure, deployment, and operations as safety-critical systems demonstrate readiness to work on frontier AI.
This section often determines whether interviewers trust you to operate models whose behavior, and misbehavior, can have real-world consequences at scale.
Section 6: Career Signals, OpenAI-Specific Hiring Criteria & Final Hiring Guidance (Questions 21–25)
By the final stage of OpenAI’s ML interview loop, interviewers are no longer assessing whether you understand training, scaling, or safety mechanisms. They are evaluating whether you can be trusted with frontier systems whose failures are difficult to predict and costly to correct. This section surfaces judgment, motivation, epistemic discipline, and alignment with OpenAI’s mission.
21. What distinguishes senior ML engineers at OpenAI from mid-level ones?
Why OpenAI asks this
OpenAI evaluates seniority differently from most companies. This question tests whether you understand what leadership looks like when working on frontier models.
How strong candidates answer
Strong candidates explain that senior ML engineers at OpenAI:
- Anticipate failure modes before they appear
- Influence training, evaluation, and deployment decisions
- Make conservative calls when uncertainty is high
- Guide safe scaling rather than chasing performance gains
They emphasize that seniority is demonstrated by preventing harm, not shipping the most impressive model.
Example
A senior engineer argues against releasing a more capable model until safety evaluations mature.
What interviewers listen for
Whether you frame seniority as foresight and restraint, not authority.
22. How do you reason under uncertainty when model behavior is poorly understood?
Why OpenAI asks this
Frontier models routinely surprise their creators. This question tests epistemic maturity.
How strong candidates answer
Strong candidates explain that uncertainty should slow decisions, not paralyze them. They discuss forming hypotheses, designing probes to reduce uncertainty, and updating beliefs when evidence contradicts assumptions.
They emphasize documenting unknowns and avoiding overconfident claims.
Example
Delaying deployment until targeted evaluations clarify unexpected behavior.
What interviewers listen for
Whether you demonstrate intellectual humility.
23. How do you handle disagreements about safety or release decisions?
Why OpenAI asks this
Safety decisions are rarely unanimous. This question tests collaboration under tension.
How strong candidates answer
Strong candidates explain that disagreements should be resolved through evidence, experiments, and shared principles, not authority. They describe escalating concerns responsibly and engaging cross-functional stakeholders when needed.
They emphasize that raising concerns is expected, not penalized.
Example
Advocating for additional red-teaming after identifying potential misuse pathways.
What interviewers listen for
Whether you prioritize mission over ego.
24. Why do you want to work on ML at OpenAI specifically?
Why OpenAI asks this
OpenAI wants candidates who understand the weight of its mission.
How strong candidates answer
Strong candidates articulate motivation rooted in responsible progress toward AGI. They express interest in understanding model behavior, improving alignment, and contributing to safe deployment, not just building powerful systems.
They avoid generic “cutting-edge” answers and demonstrate awareness of OpenAI’s societal responsibility.
Example
Wanting to work where safety and capability are treated as inseparable engineering goals.
What interviewers listen for
Whether your motivation reflects mission alignment, not prestige.
25. What questions would you ask OpenAI interviewers?
Why OpenAI asks this
This question reveals priorities and maturity.
How strong candidates answer
Strong candidates ask about:
- How OpenAI evaluates progress beyond benchmarks
- How safety tradeoffs are handled during rapid capability gains
- How teams learn from near-misses and failures
They avoid questions focused solely on speed, perks, or resume signaling.
This curiosity aligns with traits OpenAI values, similar to themes discussed in The Hidden Skills ML Interviewers Look For (That Aren’t on the Job Description).
Example
Asking how OpenAI updates evaluation strategies as models surpass existing tests.
What interviewers listen for
Whether your questions show long-term ownership mindset.
Conclusion: How to Truly Ace the OpenAI ML Interview
OpenAI’s ML interviews in 2026 are not about proving that you can optimize a model or scale infrastructure. They are about determining whether you can reason responsibly about systems whose behavior is only partially understood.
Across all six sections of this guide, several themes emerge clearly:
- OpenAI evaluates ML engineers as stewards of powerful systems, not feature builders
- Training choices are incentives that shape behavior, not neutral optimizations
- Safety and alignment are technical disciplines, not policy afterthoughts
- Seniority is inferred from judgment, humility, and restraint
Candidates who struggle in OpenAI ML interviews often do so because they over-optimize for performance while under-reasoning about behavior. They treat evaluation as static. They assume scale always helps. They answer confidently when caution is warranted.
Candidates who succeed prepare differently. They reason about incentives and emergence. They expect surprises. They treat uncertainty as a design input. They demonstrate that they understand why OpenAI moves carefully, even when faster progress is tempting.
If you approach OpenAI ML interviews with that mindset, they become demanding but coherent. You are not being tested on brilliance alone. You are being evaluated on whether OpenAI can trust you to help guide the development and deployment of increasingly capable AI systems, safely, responsibly, and thoughtfully.
