Scale AI Interview Questions — Page 6

Scale AI·Software Engineer·Technical Phone Screen

Jun 2026

Scale.ai SWE interview focused on a practical LLM API integration task, then pushed into production-readiness territory pretty quickly. The core problem was straightforward but the follow-ups are where things got real.

Given a problem that requires calling an LLM API once, implement the full solution: design the prompt, make the API call, parse the response, and return the correct output.
How would you add validation to the LLM response, including schema checks, type checks, content guardrails, and retry logic for bad outputs?
If this LLM-powered feature goes to production, what concerns would you need to address? Think about latency, cost, rate limits, error handling, prompt injection, observability, evaluation, and fallback behavior.

“The prompt design part tripped me up more than I expected.”

View Post

Scale AI·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jun 2026

Behavioral round at Scale.ai for an ML Engineer role, basically one big conflict question that took up most of the time.

Tell me about a significant disagreement you had with your manager. What was it about, how did you handle it, and what came out of it? What would you do differently?

“This one went longer than I expected.”

View Post

Scale AI·Software Engineer·Onsite - Coding / Algorithms

Jun 2026

Scale.ai coding round focused on a classic scheduling problem, the kind where you think you know it until they ask for the exact solution and you realize greedy alone won't cut it. Pretty algorithmic, no fluff.

Given a list of tasks with positive durations and K identical workers, find the minimum makespan (the earliest time all tasks finish). Tasks can't be preempted, workers handle one task at a time, and every task must be assigned to exactly one worker.
Walk through the correctness and complexity of the Longest-Processing-Time-First (LPT) approximation, and how tight is the approximation bound?

“I knew the special cases right away, K=1 means sum everything, K >= number of tasks means just take the max.”

View Post

Scale AI·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Scale.ai ML engineer coding round, done on Google Colab with some starter code already provided. Just one implementation task but it was trickier than it looked at first glance.

Using only NumPy, implement top-p (nucleus) sampling: apply softmax to a logit vector, sort by probability descending, find the smallest prefix whose cumulative probability meets or exceeds a threshold p, zero out everything outside that prefix, renormalize, and sample one token.

“The starter code had softmax and test cases already written, which honestly helped a lot.”

View Post

Scale AI·Software Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at Scale AI for a software engineer role. The whole session was basically one giant question about building a Ticketmaster-style ticketing platform under extreme concurrency. A lot of ground to cover in one sitting.

Design a high-concurrency online ticketing system similar to Ticketmaster, covering inventory management, seat reservations with expiration, payment integration, waitlist handling, and how you'd ensure no seat gets double-sold under heavy load.

“This one is massive.”

View Post

Scale AI·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026

Behavioral round at Scale.ai for a Software Engineer role, three prompts back to back. Pretty standard stuff but the timeline question tripped me up more than I expected.

What project are you most proud of? Walk through your role, why it mattered, and what you'd change if you could do it over.
Tell me about a piece of feedback you received, positive or critical. How did you take it and what actually changed because of it?
Describe a time you had to deliver under a tight deadline. How did you figure out what to cut, what to keep, and what trade-offs did you accept?

“I had a go-to answer for this but the 'what would you do differently' part always makes me second-guess myself mid-story.”

View Post

Scale AI·Software Engineer·Technical Phone Screen

Jun 2026

Scale AI coding round for a software engineer role, centered entirely on building a workflow scheduler across three progressive stages. The problem wasn't just 'implement this' but more like 'now make it better, now analyze it,' which I wasn't fully expecting.

Given a list of tasks each with a unique ID and an integer deadline but no dependencies, return the task with the smallest deadline.
Tasks now have optional prerequisites forming a DAG. Repeatedly pick the ready task with the smallest deadline, mark it done, and unlock newly eligible tasks. Return a valid execution order.
Optimize the selection of the next ready task using a more efficient data structure instead of scanning the full ready list each time. What is the time complexity of your improved solution?

“Warmup, basically.”

View Post

Scale AI·Software Engineer·Technical Phone Screen

Jun 2026

Scale.ai coding round for a software engineer role. One meaty grid problem with a clever in-place encoding trick, plus a verbal follow-up that I wasn't fully ready for. Not the hardest interview I've done but the follow-up extension caught me flat-footed.

Given an m x n binary grid representing a Game of Life board, compute the next generation state in place without using a deep copy. Use bit packing to encode both old and new states in each cell simultaneously, then shift out the old state at the end.
How would you extend this approach to handle an unbounded (infinite) grid?
Verbal follow-up: imagine a 'fire' marker that starts at a seed set of cells and spreads one step per tick to live neighbors. How would you model this per-tick expansion and encode the fire state alongside the existing cell states in place?

“I knew Game of Life but the no-copy constraint is what makes it interesting.”

View Post

Scale AI·Software Engineer·Onsite - Product Sense / Strategy

Jun 2026

Interviewed for a bizops role at Scale AI and got hit with a market sizing case about marijuana legalization. Pretty classic case format but the policy angle added a wrinkle I didn't fully anticipate.

If the US government were to legalize marijuana federally, how would you estimate the impact on consumption?

“I went straight to the supply side first which was probably backwards.”

View Post

Scale AI·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Scale AI ML engineer screen, just one question about computer vision pipelines. Pretty standard stuff but I fumbled the structure a bit under pressure.

Walk me through what a typical computer vision pipeline looks like end to end.

“I started with data collection and kind of rambled my way through preprocessing, augmentation, model training, evaluation, and deployment without really tying it together.”

View Post