EY AI Engineer interview typically runs 3 rounds: HR screen, manager interview, technical interview. It usually takes 2-4 weeks and is conversational, with a strong focus on fit and experience.
$148K
Avg. Base Comp
$208K
Avg. Total Comp
3-4
Typical Rounds
2-4 weeks
Process Length
Our candidates report that EY is looking for people who can connect AI concepts to real client work, not just recite frameworks. The strongest signal across experiences is clear, grounded explanation of your own background: one interviewer spent most of the conversation walking through the CV in detail, while another focused on how the candidate had handled software architecture and agentic AI in customer-facing settings. That tells us EY is checking for fit against the role spec as much as technical fluency, especially for more senior AI positions.
A recurring theme is the emphasis on applied AI tradeoffs. One candidate described a technical discussion centered on AI, cloud, and especially RAG, including comparisons between simpler and more complex implementations and the reasoning behind each choice. Even the lighter technical probing included fundamentals like singleton patterns, which suggests EY wants candidates who can move comfortably between modern AI systems and basic engineering concepts. The process feels less adversarial than many consulting interviews; multiple candidates noted that interviewers helped them reason through answers rather than trying to trap them.
We also see a very EY-specific pattern: the interview can feel straightforward, but the real surprise may come outside the technical conversation. One candidate said the compensation discussion was dramatically below expectations for the level, so it’s worth aligning on scope and pay early. In practice, EY seems to value candidates who are credible, client-ready, and technically solid — especially those who can explain why a solution is the right one, not just how to build it.
Synthesized from 2 candidate reports by our editorial team.
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Real interview reports from people who went through the Ey process.
I first went through a fairly linear HR interview, in which almost only the recruiter spoke and I had to tell briefly what I do and how I present myself. After that there was an interview with a manager who gave me a logic quiz; the good thing is that it was not set up aggressively, on the contrary he helped me reason step by step to the solution. The last round was the most technical, with another manager, and there we talked mainly about AI and cloud, with a very concrete focus on RAG: they asked me to compare simple RAG and more complex RAG and to discuss the tradeoffs between different techniques. In between, some more specific questions also came up, like what a singleton is, so it was not just high-level theory but also basic concepts you need to be able to explain well. Overall I found it a complete but relaxed process, more oriented toward understanding how you reason and how you move on AI topics than toward putting you in difficulty with purely algorithmic questions. In the end I received the offer, so I would say the most important thing is to arrive prepared both on your CV and on RAG, LLMs and fundamental programming concepts.
Prep tip from this candidate
Review well the tradeoffs between simple RAG and more complex RAG, because it was the center of the technical interview. Also prepare to explain your CV very concisely and to answer basic questions like singleton, plus a short logic quiz with guided reasoning.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Ey
Select the 2nd highest salary in the engineering department
| Question | |
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| Size of Joins | |
| Top 3 Users | |
| Longest Streak Users | |
| Find the First Non-Repeating Character in a String | |
| Hurdles In Data Projects | |
| Resumable Fact Table Load | |
| Skyscanner Partner ETL | |
| Sort Strings | |
| Xgboost vs Random Forest | |
| Real-Time Hashtag Partitioning | |
| Duplicate Rows | |
| Classification and Regression | |
| Pipeline Transformation Failures | |
| Mouse Search | |
| Overfit Avoidance | |
| Marketing Workflow Optimization | |
| Azure Kubernetes Infrastructure | |
| Stakeholder Communication | |
| Data Cleaning Experiences | |
| Simple Explanations | |
| Relational Migration | |
| Why Do You Want to Work With Us | |
| Processing Large CSV | |
| Your Strengths and Weaknesses | |
| 1000 Sample Classifier | |
| Quantify Uncertainty | |
| Linear vs Logistic Regression | |
| Backpropagation Explanation | |
| Rolling Bank Transactions |
Synthesized from candidate reports. Individual experiences may vary.
An initial conversation with HR or a recruiter focused on introductions, your background, and basic fit for the role. In one experience, the recruiter led most of the discussion while the candidate gave a concise overview of their experience; in another, HR later handled salary and compensation expectations.
A deeper interview with a manager or senior manager that reviews your CV and past projects in detail. The discussion centers on your software, AI, and client-facing experience, and whether your background matches the job requirements.
A more technical round with a manager that covers AI and cloud topics, with a strong focus on RAG. Candidates were asked to compare simple versus more complex RAG approaches, discuss tradeoffs between techniques, and explain foundational programming concepts such as singleton.
If you progress to the final stage, HR may discuss salary and compensation details before an offer is finalized. One candidate noted that this conversation was the point where expectations needed to be clarified early, as the compensation discussed was significantly lower than expected.
