Two Applied AI Engineer candidates describe Scale's interview as a mix of coding, LLM fundamentals and applied ML. One had a live paper discussion and a RAG coding task. The other had poker-hand coding and LLM training questions.
$203K
Avg. Base Comp
$270K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Candidates who interviewed for Applied AI Engineer roles at Scale describe a process that goes well beyond standard LeetCode. Both reports pair a coding exercise with conceptual discussion of LLMs and ML, so it helps to prepare for both.
One candidate reports a recruiter call, an object-oriented programming technical call and a manager call. A virtual onsite followed with four back-to-back interviews covering OOP, behavioral, applied ML and ML fundamentals, more than 7 hours in total. They describe a card-game OOP question and a simple RAG pipeline coding exercise. In the ML fundamentals round they had to read a research paper and answer questions about it live. They were rejected after that round.
The other candidate reports a recruiter screen, a 30-minute hiring manager call and a 30-minute technical interview. They also describe a 60-minute phone screen designing functions to evaluate poker hands, including n of a kind, straight flush, straight and flush. Wildcards such as jokers forced them to rework their approach. The technical interviewer asked about SFT, RLHF, why DPO is preferred over RLHF, model memory constraints, self-attention, pipeline parallelism versus fully sharded data parallelism, and sharding strategies. This candidate did not move forward.
To prepare, practice talking through a paper and answering follow-up questions out loud. Be ready to build a small retrieval pipeline and to handle edge cases in algorithmic code. Review LLM training methods and the tradeoffs between parallelism and sharding approaches.
Synthesized from 2 candidate reports by our editorial team.
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Featured question at Scale
Design a RAG system that restricts LLM responses exclusively to ingested data, preventing use of pre-trained knowledge.
| Question | |
|---|---|
| Spam Classifier | |
| Your Strengths and Weaknesses | |
| Random Difference | |
| Empty Neighborhoods | |
| Merge Sorted Lists | |
| 2nd Highest Salary | |
| Monthly Customer Report | |
| Top Three Salaries | |
| First Touch Attribution | |
| First to Six | |
| String Shift | |
| 500 Cards | |
| Hurdles In Data Projects | |
| Impression Reach | |
| Size of Joins | |
| Last Transaction | |
| Top 3 Users | |
| Raining in Seattle | |
| Third Purchase | |
| Bank Fraud Model | |
| Job Recommendation | |
| Minimum Change | |
| Total Spent on Products | |
| Jars and Coins | |
| Lazy Raters | |
| Longest Increasing Subsequence | |
| Find the First Non-Repeating Character in a String | |
| Fair Coin | |
| Daily Retention Summary |
Synthesized from candidate reports. Individual experiences may vary.
Both candidates began with a recruiter conversation. Neither account describes what was discussed, so there is little to go on beyond having a clear summary of your applied AI background and your interest in the role ready.
The coding screens differed between the two reports. One candidate had an object-oriented technical call with a card-game style question. The other had a 60-minute phone screen designing poker hand evaluation functions, where jokers as wildcards forced a rethink of the algorithm.
One candidate mentions a manager call without giving its length. The other describes a 30-minute call of rapid-fire questions like 'are you familiar with X?' that felt rushed and lacked follow-up. Be ready to say briefly which techniques you have actually used.
One candidate describes a structured 30-minute technical interview covering SFT, RLHF, why DPO is preferred over RLHF, model memory constraints, self-attention, pipeline parallelism versus fully sharded data parallelism, and sharding strategies, with follow-ups probing their reasoning.
One candidate reports a virtual onsite of four back-to-back interviews: technical OOP, behavioral, applied ML and ML fundamentals, over 7 hours of interviewing in total. Applied ML included coding a simple RAG pipeline, and the fundamentals round was a live research paper discussion.
