How Instacart Interviews in 2026: Process, Questions & What They Score

1. 01

   Implement a function to parse a text file/API input and aggregate data based on specific rules (multi-part problems where each part builds on the previous solution)

   Multi-part implementation problem: parsing input, aggregating by rules, and extending cleanly as parts build on each other.

2. 02

   Design a data structure that supports set, get, and get_at_time, i.e., a time-based key-value store

   Data-structure design: per-key timestamped values with binary search for get_at_time.

3. 03

   String manipulation problems involving anagrams or palindromes under time-complexity constraints

   String manipulation under complexity constraints: frequency counting and two-pointer checks.

4. 04

   Currency conversion / exchange-rate calculation problems

   Graph problem in disguise: model rates as edges and traverse to chain conversions.

5. 05

   Edit distance and other classic dynamic-programming-adjacent problems (asked at a practical rather than academic level)

   Classic DP on two strings; a practical working table matters more than theory here.

6. 06

   Problems built around binary trees, hash tables, and matrices requiring you to read/parse input and process it end to end

   End-to-end implementation: parsing raw input into trees, maps, or matrices and processing it completely.

7. 07

   Two-pointer, DFS/BFS traversal problems on arrays, strings, or graphs

   Pattern staples: two pointers on arrays and strings, DFS/BFS traversal on graphs.

8. 08

   Design and implement a key-value store with delete/expiration or versioning semantics (a generalization of the time-based store question; framed by some prep sites as a 'rate limiter'-style extension, though that specific framing is less directly corroborated than the other questions here)

   Extends a key-value store with TTL and versioning semantics; lazy versus active expiry design.

9. 09

   Note: Consistently described across sources as 'LeetCode Medium' in difficulty but weighted toward working, practical implementation (parsing input, handling edge cases) over obscure algorithms or advanced trees/DP.

Reported problems, listed so you know what to expect. Practice them in your own editor or on the platform you prefer; SupaCV's practice mode coaches how you talk through them.

1. 01

   Design a real-time order management/fulfillment system covering order placement, inventory, shopper assignment, routing, and notifications

   System design: order flow, inventory, shopper assignment, routing, and notifications for a live marketplace.

2. 02

   How would you ensure inventory/product-catalog accuracy across thousands of stores in near-real-time?

   Freshness-at-scale design: ingestion pipelines, staleness bounds, and reconciliation across thousands of stores.

3. 03

   Design a delivery-tracking / real-time shipment status service that provides customers with accurate ETAs

   System design: location ingestion, streaming updates, and serving accurate customer-facing ETAs.

4. 04

   Design a secure, scalable payment-processing system for a marketplace, including handling payment-processor failures with idempotency

   Payment reliability: idempotency keys, processor-failure handling, and secure marketplace money flow.

5. 05

   Design a backend for a 'flash sale' feature that must handle rapid inventory depletion without overselling

   Contention handling: atomic inventory decrements, queueing, and overselling prevention under spike load.

6. 06

   Design a database schema/service for a library-style rental or catalog system (a recurring 'generic' prompt used to probe data modeling)

   Data-modeling probe: entities, relationships, and constraints for a catalog and rental domain.

7. 07

   Design a personalization/recommendation system for grocery substitutions or product suggestions

   System design with ML flavor: candidate retrieval, ranking, and substitution logic for groceries.

8. 08

   Note: Consistently described as a 45-60 minute open-ended, diagram-driven discussion (candidates can use tools like Excalidraw/Google Draw) evaluating trade-off reasoning (SQL vs NoSQL, sharding strategy), scalability, reliability/failover, and tying architecture back to customer-facing outcomes. Multiple sources flag this round as a common point of failure for reaching senior/staff-level offers even when coding rounds go well.

Reported problems, listed so you know what to expect. Practice them in your own editor or on the platform you prefer; SupaCV's practice mode coaches how you talk through them.

1. 01

   Write a query to compute total orders, total product sales, and month-over-month growth rate for each product.

   SQL aggregation with month-over-month growth: monthly rollups plus a window function like LAG.

2. 02

   Find the most frequently purchased item at each store, given store/item/order tables.

   Per-group top-1 SQL problem: ROW_NUMBER or RANK partitioned by store.

3. 03

   Calculate click-through rate (add-to-carts / clicks) and conversion rate (purchases / add-to-carts) for products.

   Funnel-metric SQL: ratios across clicks, add-to-carts, and purchases with careful denominators.

4. 04

   Calculate reorder rate or average basket size from raw order-line data using window functions/CTEs.

   Window functions and CTEs over order-line data to compute reorder and basket metrics.

5. 05

   Identify products that were reordered fewer than a threshold in the past but are now trending toward a higher reorder rate (a case-study-style prompt used by Instacart-focused prep sites, e.g. DataLemur's Instacart case study).

   Case-style SQL: comparing past and recent reorder rates per product to surface trend changes.

6. 06

   Design and reason through an A/B test for a marketplace feature, including sample-size calculation, statistical significance, and pitfalls like peeking or network effects between shoppers and customers.

   Experimentation depth: sample size, significance, peeking, and marketplace network effects.

7. 07

   Note: Data Science candidates report two separate 45-minute technical screens: one weighted toward SQL/product-case reasoning, the other toward statistics/experimentation. Multi-table joins, window functions, and CTEs are repeatedly cited as required skills; the emphasis is on business analytics and experimentation rather than machine-learning theory.

Reported problems, listed so you know what to expect. Practice them in your own editor or on the platform you prefer; SupaCV's practice mode coaches how you talk through them.

1. 01

   What are the three biggest things you'd change about the Instacart app (or a competitor's) to improve it, and why?

2. 02

   How would you measure the success of a new feature (e.g., a substitution or recommendation feature)? Which metrics would you track and why?

3. 03

   Walk me through how you'd prioritize a roadmap when customer needs and business goals about a specific Instacart feature conflict.

4. 04

   How would you balance a retailer partner's interests against a shopper's and a customer's when designing a feature?

1. 01

   Why do you want to join Instacart?

2. 02

   Tell me about the most challenging project you've worked on and the impact it had (be ready with quantified/numerical impact).

3. 03

   Tell me about a time you had a conflict with a coworker and how you resolved it.

4. 04

   Describe a time you had to manage a tight deadline with ambiguous requirements.

5. 05

   Tell me about a time you disagreed with your manager or a stakeholder about a strategic direction based on data.

6. 06

   Describe a project where you went above and beyond, both technically and in terms of business impact.

7. 07

   Tell me about a process or tool you learned/built at a previous job that would be valuable at Instacart.

8. 08

   Note: This round is described consistently (Blind, Glassdoor, TechPrep, Dataford, interviewing.io) as being run by a senior engineer or manager from a different team than the hiring team, explicitly to assess ownership/leadership/impact and to provide an independent read on the candidate separate from the hiring manager.

1. 01

   Tell me about a time you identified a customer need and shipped a product/feature to address it.

2. 02

   Tell me about a product you managed that failed: what did you learn?

3. 03

   Note: PM loops are team-specific (you interview for a specific leveled role on a specific team). Reported onsite rounds typically include a product-sense interview, a product-execution/metrics interview with another PM, a cross-functional panel, and often a separate leadership/behavioral round; naming and count vary by source and team.