Medium Software Engineer Interview Guide

The 60-second brief

As a Software Engineer at Medium, you play a vital role in building and scaling the publishing platform that empowers millions of readers and writers worldwide. Your work directly influences core product infrastructure, content delivery systems, and user engagement features that define the reading and publishing experience. This position is critical to maintaining a robust, performant ecosystem where ideas can be shared seamlessly on a global scale. You will contribute to high-impact problem spaces ranging from backend architecture and content distribution to real-time collaboration tools and platform scalability. The engineering culture values clean code, thoughtful architecture, and user-centric problem solving over rushed feature delivery.

This guide is scoped to a Software Engineer candidate at Medium.

Focus areas

• System Design
• Retrieval-Augmented Generation (RAG)
• Natural Language Processing (NLP)

The Medium interview process

Medium candidates report 4 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Recruiter Screen

Initial discussion about your background, interest in Medium, and role alignment.

What to demonstrate

• Initial discussion about your background, interest in Medium, and role alignment
• Depth in System Design

How to prepare

• Be able to walk your CV end to end in two minutes, and say why this company specifically.
• Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.

Take-Home Assignment

Practical coding assignment focusing on data structures and manipulation.

What to demonstrate

• Practical coding assignment focusing on data structures and manipulation
• Depth in System Design

How to prepare

• Answer aloud and timed: Expand an existing data structure solution to include an audit trail or edit history of the provided data.
• Answer aloud and timed: Solve algorithmic data structure challenges focusing on efficiency and edge-case handling.

Hiring Manager Screen

Behavioral and situational questions using the STAR format.

What to demonstrate

• Behavioral and situational questions using the STAR format
• Depth in System Design

How to prepare

• Prepare two projects you led end to end, each with the decision you owned and what it cost.
• Have three questions about the team's roadmap and how success is measured in the first six months.

Virtual Onsite

Comprehensive interview including behavioral rounds, live coding, and system design.

What to demonstrate

• Comprehensive interview including behavioral rounds, live coding, and system design
• Depth in System Design

How to prepare

• Answer aloud and timed: Design a core feature typically found on a content publishing platform, such as a content recommendation feed or comment aggregation service.
• Answer aloud and timed: Outline a scalable architecture for handling millions of daily active readers and writers with minimal latency.

Where candidates lose points

Emphasize code cleanliness

Interviewers place high value on readable, well-structured code. Always write meaningful variable names and modular functions during live coding sessions.

Clarify requirements early

Never jump straight into coding or designing a system without asking clarifying questions about scale, constraints, and edge cases.

Communicate your trade-offs

In system design rounds, there is rarely a single right answer. Explicitly state the pros and cons of your architectural choices.

Prepare STAR-format behavioral stories

Keep a few concrete examples ready that showcase your ownership, conflict resolution, and collaboration skills.

Questions & practice

Implement a classic game like tic-tac-toe or hangman with clean, extensible code.

Medium · Coding and Data Structures

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. State the target complexity and say which constraint rules the naive version out.

Follow-up

• How does this change if the input no longer fits in memory?
• What is the worst case, and how likely is it on real data?

Write a data manipulation script that processes input logs and outputs summarized analytics.

Medium · Coding and Data Structures

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. State the target complexity and say which constraint rules the naive version out.

Follow-up

• How does this change if the input no longer fits in memory?
• What is the worst case, and how likely is it on real data?

Expand an existing data structure solution to include an audit trail or edit history of the provided data.

Medium · Coding and Data Structures

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. State the target complexity and say which constraint rules the naive version out.

Follow-up

• How does this change if the input no longer fits in memory?
• What is the worst case, and how likely is it on real data?

Solve algorithmic data structure challenges focusing on efficiency and edge-case handling.

Medium · Coding and Data Structures

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. State the target complexity and say which constraint rules the naive version out.

Follow-up

• How does this change if the input no longer fits in memory?
• What is the worst case, and how likely is it on real data?

Write robust code during a live pair-programming session using collaborative coding environments.

Medium · Coding and Data Structures

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. State the target complexity and say which constraint rules the naive version out.

Follow-up

• How does this change if the input no longer fits in memory?
• What is the worst case, and how likely is it on real data?

Discuss how you would structure database schemas and caching layers for fast content retrieval.

Medium · System Design and Architecture

Approach

1. Name the grain you start from and join outward from it.
2. Check whether any join is one-to-many before aggregating, or the sums inflate.
3. Say which index the query would use, and what makes it unusable.
4. Handle the rows that do not match: that is usually the actual question.

Follow-up

• How does the query change if that join becomes one-to-many?
• What happens to this when the table is ten times larger?

Explain why the owner filter ignores the listing index

Medium · composite index · query plans · sort elimination

The only index on resource is (tenant_id, status, updated_at DESC, resource_id DESC). A new endpoint returns one user's resources across all statuses, newest created first: WHERE tenant_id = $1 AND owner_user_id = $2 ORDER BY created_at DESC LIMIT 20. On a tenant with 2M rows it takes 900 ms and EXPLAIN shows a sort above a large scan. Explain precisely why the existing index cannot serve it, give the index that can, and state which of these the new index still will not help: owner_user_id alone across tenants; the same query ordered by updated_at. PostgreSQL 16.

Approach

1. Separate the two jobs an index does. For filtering, a composite btree is seekable only on a left prefix, so with no predicate on status the scan can at best range over tenant_id and test owner_user_id per row; PostgreSQL 16 has no btree skip scan to jump the unconstrained column.
2. For ordering, the index is sorted by (status, updated_at) within a tenant and not by created_at, so the LIMIT cannot stop early: every matching row is read and then sorted. That is the 'Sort Method: top-N heapsort' line, and it is why the plan reads 2M rows to answer with 20.
3. Derive the replacement from the access path — equality, equality, then the ordering column: CREATE INDEX CONCURRENTLY ON resource (tenant_id, owner_user_id, created_at DESC). The scan seeks to the (tenant, owner) range and walks 20 entries in order, so the Sort node disappears along with the row-read.
4. Treat INCLUDE (title, status) as conditional, not free. An index-only scan still visits the heap for any row whose page is not marked all-visible, so on a table taking 1.2k writes/second the win depends on autovacuum keeping the visibility map current, and the wider index costs more on every insert.

Follow-up

• 90% of rows are status='active'. Would a partial index WHERE status = 'active' change your answer, and for which of the three queries?
• A dashboard runs this for 40 owners in one page load. What changes about the design?

Design an elevator control system or a similar real-world mechanical scheduling platform.

Medium · System Design and Architecture

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. State the consistency you need, and where you are willing to be stale.

Follow-up

• What breaks first when traffic grows ten times?
• How does this behave when that dependency is down for an hour?

Design a core feature typically found on a content publishing platform, such as a content recommendation feed

Medium · System Design and Architecture

Design a core feature typically found on a content publishing platform, such as a content recommendation feed or comment aggregation service.

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. State the consistency you need, and where you are willing to be stale.

Follow-up

• What breaks first when traffic grows ten times?
• How does this behave when that dependency is down for an hour?

Outline a scalable architecture for handling millions of daily active readers and writers with minimal latency

Medium · System Design and Architecture

Outline a scalable architecture for handling millions of daily active readers and writers with minimal latency.

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. State the consistency you need, and where you are willing to be stale.

Follow-up

• What breaks first when traffic grows ten times?
• How does this behave when that dependency is down for an hour?

Read latency spikes on a sixty-second sawtooth

Medium · cache stampede · single-flight · ttl

The cached listing read path serves about 14k reads/second at an 85% hit rate. p99 sits at 35 ms for 57 seconds, jumps to 900 ms for 3, and repeats. During each spike the primary shows several hundred identical listing queries starting within the same millisecond, all carrying one large tenant's id. Cache entries use a 60-second TTL. Give the mechanism, the ordered checks, the fix, and the correctness hazard your fix must not introduce.

Approach

1. Match the period to a configured number before theorising about load. A spike every 60 seconds against a 60-second TTL is an entry expiring, and you confirm it by correlating spike timestamps with the entry's write time rather than with the traffic curve. If the period had matched a cron or a GC interval instead, this is a different investigation.
2. Establish the concurrency of the miss. Several hundred identical queries in one millisecond means the miss path has no coalescing: every request that arrives between expiry and repopulation recomputes. The herd size is that key's arrival rate times its recompute time, so at 1.2k reads/second for the hot key and a 250 ms recompute you expect about 300 concurrent misses, which matches what is observed.
3. Add single-flight on the miss path so one caller per key recomputes under a short-lived lock while the rest wait for its result. Prefer stale-while-revalidate where the read tolerates it: return the expired value immediately and refresh asynchronously, which removes the latency spike rather than serialising it into a queue of waiters.
4. De-synchronise the keys. Write TTLs with jitter, for example 60 seconds plus or minus 10%, so a deploy or a mass invalidation does not align every key on the same second and turn a per-key herd into a fleet-wide one.

Follow-up

• The same sawtooth appears on a key that is invalidated on write rather than expired. Is that the same bug?
• How does your answer change if the recompute takes 4 seconds instead of 250 ms?

Suggested practice checklist

Built from the rounds and topics Medium candidates report.

Day 1 — Map the Medium loop

• Write out the reported sequence: Recruiter Screen, Take-Home Assignment, Hiring Manager Screen, Virtual Onsite.
• For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.

Deliverable: A one-page map of the 4 reported rounds, with the weakest marked.

Day 2 — Work System Design

• Spend the session on System Design, which Medium candidates report being tested on.
• Write one worked example in System Design and time yourself on it.

Deliverable: One timed worked example in System Design.

Day 3 — Work Retrieval-Augmented Generation (RAG)

• Spend the session on Retrieval-Augmented Generation (RAG), which Medium candidates report being tested on.
• Write one worked example in Retrieval-Augmented Generation (RAG) and time yourself on it.

Deliverable: One timed worked example in Retrieval-Augmented Generation (RAG).

Day 4 — Work Natural Language Processing (NLP)

• Spend the session on Natural Language Processing (NLP), which Medium candidates report being tested on.
• Write one worked example in Natural Language Processing (NLP) and time yourself on it.

Deliverable: One timed worked example in Natural Language Processing (NLP).

Day 5 — Answer out loud: Coding and Data Structures

• Answer aloud, timed: Implement a classic game like tic-tac-toe or hangman with clean, extensible code.
• Answer aloud, timed: Write a data manipulation script that processes input logs and outputs summarized analytics.

Deliverable: Spoken answers to 2 reported Coding and Data Structures question(s), under time.

Day 6 — Answer out loud: System Design and Architecture

• Answer aloud, timed: Design an elevator control system or a similar real-world mechanical scheduling platform.
• Answer aloud, timed: Design a core feature typically found on a content publishing platform, such as a content recommendation feed or comment aggregation service.

Deliverable: Spoken answers to 2 reported System Design and Architecture question(s), under time.

Day 7 — Answer out loud: Behavioral and Culture Fit

• Answer aloud, timed: Tell me about your background and why you are specifically interested in joining Medium.
• Answer aloud, timed: Describe a time when you collaborated across functional teams to deliver a complex technical project.

Deliverable: Spoken answers to 2 reported Behavioral and Culture Fit question(s), under time.

Behavioural preparation

Behavioural rounds judge the decision you made and what it cost.

Tell me about your background and why you are specifically interested in joining Medium.

Medium · Behavioral and Culture Fit

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

Describe a time when you collaborated across functional teams to deliver a complex technical project.

Medium · Behavioral and Culture Fit

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

How do you handle technical disagreements or ambiguity when designing a new feature?

Medium · Behavioral and Culture Fit

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

Walk through a past project where you had to balance engineering speed with long-term code maintainability.

Medium · Behavioral and Culture Fit

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?