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Instacart interview notes, page 13 of 13

Large Enterprises

Most of these Instacart write-ups come from Software Engineers (88), then Data Scientists (28) and Machine Learning Engineers (5). The most reported rounds are the technical phone screen (93), the system design round (13), the behavioral / leadership round (6) and the product sense round (6).

128 interview notes · updated Jul 2026

Instacart·Software Engineer·Technical Phone Screen

Jul 2026
Instacart software engineer interview with a meaty data structures problem that had me thinking about tries for way longer than I expected. The complexity analysis portion caught me a bit flat-footed.
  • Design a data structure with an add(name) method and a find(prefix, suffix) method that returns all stored file names matching both a given prefix and suffix, in lexicographic order. Discuss time and space complexity and compare approaches like dual tries, suffix automata, and inverted indexes. Assume up to 100,000 insertions and frequent queries.

“I went straight for the two-trie approach: one trie for prefixes, one for reversed strings to handle suffixes, then intersect the result sets.”

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Instacart·Data Scientist·Technical Phone Screen

Jul 2026
SQL-heavy technical screen for a data scientist role at Instacart. One question, focused on time-series aggregation over order data. Nothing too wild but you need to be comfortable with date functions or you'll fumble it.
  • Write a SQL query that returns total revenue and order count for the last 8 full calendar weeks, filtering to standard delivery orders only.

“The filter part is easy, the tricky bit is getting 'last 8 full calendar weeks' right.”

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Instacart·Software Engineer·Onsite - System Design / Architecture

Jul 2026
System design round at Instacart for a software engineering role. The whole session was basically one giant design problem broken into layers, and they kept pushing deeper on every answer I gave. Felt like I was treading water by the end.
  • Design a multi-tenant cloud storage service that supports uploading, retrieving, and copying files. How would you define the APIs, and what copy semantics would you use (server-side copy, deep copy, copy-on-write)? How does metadata and permissions propagation work across copies?
  • How would you handle per-user storage quotas and tiered capacity limits? Walk through quota accounting, how you enforce limits at upload time, and how copies and compressed files count toward a user's quota.
  • How would you implement file compression on upload and decompression on download? Which algorithms would you choose, where in the data path does compression happen, and what are the trade-offs between CPU cost, latency, and storage savings?
  • How do you ensure durability, availability, scalability, and consistency in this storage system? Describe the components you'd use, how you'd partition and replicate data, and how you'd handle failures and background recovery tasks.
  • How would you approach security, auditing, versioning, and monitoring for this service? Propose specific APIs or methods for each and describe how you'd test them.

“Started okay with the basic CRUD APIs but the copy semantics part tripped me up.”

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Instacart·Data Scientist·Technical Phone Screen

Jul 2026
Instacart data science interview with a heavy stats/modeling focus. The core question was about a regression model with a terrible R² and what to do about it, which sounds simple but has a lot of moving parts once you get into inference vs prediction tradeoffs.
  • You have a linear regression predicting contribution per order with an R² of 0.07. What concrete steps would you take to improve predictive performance without breaking the validity of your inference?
  • Does adding more covariates reliably increase out-of-sample R²? Walk through how you'd demonstrate this with cross-validation, and what modeling alternatives you'd consider if the goal is still effect estimation.
  • How would you use nested cross-validation and target-leakage tests to prevent p-hacking when iterating on model specifications?
  • When is a low R² acceptable for estimating an average treatment effect, and when does it become a real problem for making individual-level predictions?

“This is where I probably spent too long listing things without prioritizing.”

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Instacart·Data Scientist·Take-home Assignment

Jul 2026
Take-home assignment for a DS role at Instacart centered entirely on one big A/B test case about a checkout change for same-day delivery. Dense, multi-part, and the kind of thing that looks manageable until you're three hours in and second-guessing your variance assumptions.
  • For each metric in the A/B test (conversion rate, AOV, take rate, delivery cost per order), which hypothesis test would you use and why? Walk through the distributional assumptions and how you'd validate them.
  • Define contribution per order as GMV times take rate minus delivery cost. How would you model delivery cost scaling with order volume and take rate saturation at high discounts, and how do you propagate uncertainty through to a final decision?
  • Design a segmentation plan to detect heterogeneous treatment effects across dimensions like market, time of day, basket size, and new versus repeat users. How do you control for multiple comparisons while still surfacing segments that are actually useful?
  • Given the operational risk of manually scaling shopper supply, what optimization objective and constraints would you propose to balance short-term revenue lift against service-level metrics like cancellation rate?

“This is where I spent most of my time and probably overcomplicated it.”

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Instacart·Software Engineer·Onsite - System Design / Architecture

Jul 2026
System design round at Instacart for a software engineer role. The whole thing was basically one giant question about inventory management, and they wanted you to go deep on pretty much every layer of the stack. Felt like they were testing whether you could hold a lot of complexity in your head at once without losing the thread.
  • Design a scalable, highly available inventory management system for an e-commerce platform that tracks stock across multiple warehouses, supports Reserve, Release, and Purchase operations, prevents overselling under concurrent load, and can handle flash-sale traffic spikes up to 50k RPS reads and 5k RPS writes.
  • How would you handle consistency trade-offs in this system, specifically when to use strong consistency versus eventual consistency and why?
  • Walk through your strategy for preventing race conditions when multiple concurrent requests try to reserve the same inventory item.
  • How would you design the caching layer for product availability reads, and how would you handle cache invalidation?
  • How would you ensure idempotency for the Reserve, Release, and Purchase APIs and provide at-least-once delivery semantics for order events?
  • Describe your approach to scaling the write path, including sharding, write queues, and CQRS if applicable.
  • How would you handle partial failures and retries across this system, and how do you achieve exactly-once semantics at critical boundaries?
  • What observability strategy would you put in place for this system, and how would you approach disaster recovery and multi-region deployment?

“This was the whole interview.”

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Instacart·Data Scientist·Technical Phone Screen

Jul 2026
Instacart data scientist interview with a deep-dive technical question on large-scale data pipeline design. Single question but it covered a lot of ground, felt more like a system design session than a typical DS screen.
  • Walk through a project where you ingested and processed a dataset of 500 million or more rows (or over 1 TB) end-to-end. Cover storage formats, partitioning, memory and compute constraints, schema evolution, data quality checks, indexing, and your tool choices. Share before/after runtimes and cost, a specific code-level optimization you applied, and how your approach would change if you were limited to a single 32 GB RAM machine.

“This one sprawled in every direction.”

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Instacart·Data Scientist·Technical Phone Screen

Jul 2026
Instacart data science interview focused on a classic metric decline investigation scenario. The whole thing was essentially one big open-ended case, and they wanted to see how structured your thinking was across data quality, segmentation, and hypothesis generation.
  • A key business metric (like orders per day or conversion rate) has dropped noticeably over the past week or two. Walk through how you'd investigate it.

“This is the kind of question where you can go wide fast and lose the thread entirely.”

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