DoorDash Machine Learning Engineer interview questions

25 Machine Learning Engineers have shared notes from their DoorDash interviews, with the questions they were asked in each round. That is 6% of the 416 DoorDash interview notes on Screna AI.

25 Machine Learning Engineer interview notes

DoorDash·Machine Learning Engineer·Technical Phone Screen

Jul 2026
DoorDash ML engineer screen, just one coding problem the whole time. Pretty straightforward BST question but the floating-point angle tripped me up for a second.
  • Given a binary search tree and a floating-point target value, find the node value in the tree that is closest to the target. If two values are equally close, return the smaller one.

“The BST traversal part was fine, I've done that before.”

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DoorDash·Machine Learning Engineer·Technical Phone Screen

Jul 2026
DoorDash ML engineer screen with a grid/heatmap coding problem. Pretty algorithmic for an MLE role but the follow-up about optimizing with a 2D difference array is what made it interesting.
  • Given an n x n grid and a list of restaurants, each defined by a position, a radius, and a delivery count, compute a heatmap showing the expected number of deliveries at each cell. Each restaurant contributes its delivery count to every cell within a (2r+1) x (2r+1) square centered on it, clipped to the grid boundaries.

“The naive approach is obvious, just loop over each restaurant and fill in the square.”

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DoorDash·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round at DoorDash for an ML Engineer role. Just the one question but it had a lot of moving parts packed into it.
  • Tell me about a time you had a disagreement with a collaborator. How did you approach the conversation, what was the disagreement about, how did you understand their perspective, and how did you resolve it? What was the outcome, and what would you do differently?

“The question itself is fine but they really loaded it up.”

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DoorDash·Machine Learning Engineer·Technical Phone Screen

Jun 2026
DoorDash MLE interview with a meaty operational scenario about wrong-order incidents on a food delivery platform. The question covered a lot of ground and felt more like a product case than a pure ML problem, which I wasn't fully expecting.
  • You work on a food delivery platform where customers sometimes receive wrong items. How would you maximize the reduction of wrong-order incidents, covering root cause diagnosis, process and technical changes, success metrics, trade-offs, and safe rollout?

“This question is wide.”

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DoorDash·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026
ML system design round at DoorDash for an MLE role. The whole thing was focused on infrastructure and pipelines rather than modeling, which I wasn't fully expecting. Pretty thorough coverage of the full lifecycle from feature stores to serving to observability.
  • Design the end-to-end ML infrastructure for a large-scale product, covering the training pipeline, inference pipeline, and experiment management. Focus on the infrastructure and high-level pipelines rather than the model itself.
  • How would you design a feature store that supports both online low-latency lookups and offline batch training jobs?
  • Walk through how you'd handle A/B testing, shadow deployments, canary rollouts, and rollbacks for ML models in production.
  • How would you monitor a deployed ML system for data drift, model drift, and training/serving skew?
  • How would you approach cost optimization and multi-tenant isolation in a shared ML infrastructure platform?

“This was basically the whole interview.”

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DoorDash·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Behavioral and technical mix for an MLE role at DoorDash. The conversation covered hands-on LLM work, a resume walkthrough, and a pretty open-ended discussion about what research directions you actually care about and why. Felt more like a vibe check on depth than a grind-through-leetcode situation.
  • Walk us through your hands-on experience with LLMs, covering training, fine-tuning, prompting, and deployment.
  • Which LLM research directions do you find most interesting, and why?
  • Take us through the highlights of your resume.

“This is where I spent most of the call.”

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DoorDash·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026
DoorDash MLE system design round focused on ads infrastructure end to end. No ML theory, just backend architecture for the whole ads pipeline, which was a nice change from the usual.
  • Design an ads recommendation and serving system, focusing on the backend infrastructure rather than the ML model itself. Cover ad ingestion, candidate retrieval, ranking, serving top-N ads, click and impression tracking, budget pacing, and the components that support all of this at scale.

“This is a big question and I underestimated how much ground it covers.”

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DoorDash·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Interviewed for an ML engineer role at DoorDash and got asked about AWS experience in the context of machine learning work. Pretty standard technical screen, nothing too wild.
  • How have you used AWS services in your machine learning projects?

“Talked through a few projects where I used SageMaker and S3, maybe leaned too hard on the infrastructure side and not enough on the actual modeling decisions.”

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DoorDash·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026
DoorDash ML Engineer onsite focused entirely on designing a homepage recommendation system. One long, dense question that basically covered everything from retrieval to ranking to cold-start. Felt like a mini system design marathon.
  • Design a personalized homepage recommendation system that retrieves and ranks a feed for each user, covering requirements, data sources, retrieval and ranking stages, feature infrastructure, serving, training, evaluation, and strategies for cold-start and diversity.

“This thing sprawled fast.”

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DoorDash·Machine Learning Engineer·Technical Phone Screen

May 2026
DoorDash ML Engineer interview that was basically one long project deep-dive. They pick something off your resume and just keep pulling threads until you run out of answers.
  • Walk me through a project you worked on. Be ready for detailed follow-ups on your specific contributions, the technical decisions you made, alternatives you considered, who you worked with, what went wrong, and what the measurable outcome was.

“This wasn't a soft opener.”

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