← Back to Directory

Roku interview notes, page 2 of 2

Large Enterprises

Screna has 16 Roku interview write-ups. The most reported round is the technical phone screen (8).

16 interview notes · updated Jul 2026

Roku·Data Scientist·Technical Phone Screen

Jun 2026
Roku data science interview with a stats intuition probe. Pretty conversational, but they really wanted you to simplify without losing the point.
  • How would you explain the bias-variance trade-off to someone without a technical background?

“I went with an analogy about fitting a curve through data points and rambled a bit about underfitting before getting to the balance part.”

View Post

Roku·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Roku system design round for a software engineer position. The main problem was building a thread-safe LRU cache from scratch, which sounds manageable until they start pushing on concurrency trade-offs and you realize how much there is to actually say.
  • Design and implement a thread-safe LRU cache with get and put operations that both run in O(1) time and can be called concurrently from multiple threads.

“I went with a hashmap plus doubly linked list, slapped a global mutex around everything, and called it thread-safe.”

View Post

Roku·Software Engineer·Technical Phone Screen

Jun 2026
Roku SWE interview split into two chunks: one where you walk through a project you led, and one where they put multithreaded code in front of you and ask you to tear it apart. The concurrency section can get pretty gnarly depending on what snippet they pick.
  • Walk us through a technical project you led, covering the architecture, the key decisions you made, the trade-offs involved, and what your specific contributions were.
  • Here is a short multithreaded code snippet. Identify any correctness or performance problems, explain the underlying memory model reason, and propose a fix.

“This part felt fine until they started poking at the trade-offs.”

View Post

Roku·Software Engineer·Technical Phone Screen

Jun 2026
Roku SWE interview with a streaming/filtering problem that looks easy but has some gotchas worth thinking through. Pretty standard technical screen setup, one question, asked to walk through the solution and talk about testing.
  • Given a stream of ads where each ad has a topic and a company, filter the stream so that you only keep ads whose topic and company haven't appeared in any previously accepted ad. Solve it in O(N) time and be ready to discuss test cases.

“Two sets, one for topics one for companies, check both before accepting and add to both after.”

View Post

Roku·Data Scientist·Take-home Assignment

Jun 2026
Roku data science take-home, three SQL problems back to back with a shared schema. The questions were genuinely hard and I spent way longer on them than I expected. Not a vibe check, they clearly wanted to see if you could actually write production-grade SQL.
  • Given a schema with orders, order items, and refunds, write SQL to compute a 7-day rolling gross profit by product category bucket (Speakers vs everything else) for each calendar day in a specified date range. Treat refunds as negative profit applied on the refund date, not the order date.
  • For non-test, non-cancelled orders, identify the top 2 products by week-over-week revenue growth comparing a specific 7-day window to the prior 7 days. Refunds count as negative revenue on the refund timestamp date.
  • For users whose first event date falls within a given 6-day window, calculate D1 retention rate by country. D1 means the user had any event exactly one calendar day after their first event date.

“The rolling window part was fine, lag/lead stuff I can do in my sleep.”

View Post

Roku·Data Scientist·Technical Phone Screen

Jul 2026
Roku data scientist interview with a business case question about diagnosing a profit drop at a coffee shop. Pretty classic case framing but they clearly wanted to see structured thinking, not just gut instinct.
  • You're managing a coffee shop that's been losing profit lately. Walk through how you'd systematically figure out why, including what data you'd pull, which metrics you'd calculate, and what hypotheses you'd actually test.

“I started with revenue and immediately went into ticket size and customer volume, which felt right, but I forgot to anchor the funnel properly before jumping to hypotheses.”

View Post