Roku Machine Learning Engineer Interview: Questions & Prep (2026)

Roku builds the leading streaming platform in the US, and its ML team works on the systems that decide what content appears on your home screen, how ads are targeted, how search results are ranked, and how new users discover shows they will love. The ML Engineer role sits at the centre of these efforts, requiring a blend of applied machine learning, software engineering, and a clear sense of how recommendation quality connects to viewer engagement and ad revenue.

Candidates report the interview process typically includes a recruiter screen, a technical phone screen covering ML concepts and coding, and a full virtual loop with separate panels for ML system design, coding, and behavioural questions. The process typically takes two to four weeks from first contact to offer. As of July 2026, knok tracked 253 open roles at Roku across all functions, signalling active hiring across levels.

Based on candidate reports and Roku's public focus areas in streaming, personalisation, and ad tech, these questions come up most often in ML Engineer interviews at Roku.

1. How would you design a recommendation system for Roku's home screen content feed, from data collection through to serving?
2. Roku's platform serves millions of daily active users. How do you handle cold-start for new users and new titles?
3. Walk me through how you would set up an A/B test to evaluate a new ranking model. What metrics would you track, and why?
4. How do you detect data drift in a production model, and what steps do you take when drift is confirmed?
5. Explain the trade-offs between batch and real-time inference. When would you choose each for a content ranking use case?
6. How would you design a click-through rate prediction model for streaming video ads?
7. What is your approach to feature engineering for a user-item collaborative filtering system?
8. How do you balance exploration and exploitation in a live recommendation system?
9. Walk me through a time you debugged a model that performed well offline but poorly in production.
10. How would you design a feature store for an ML platform serving multiple product teams?
11. How do you approach fairness and content diversity in a content recommendation ranker?
12. Describe a time you influenced a product or business decision using ML results. How did you communicate the trade-offs to non-technical stakeholders?

Three STAR-format sample answers for the questions candidates report facing most often.

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Q: Walk me through a time you debugged a model that performed well offline but poorly in production.

*Situation:* At my previous company, we launched a new ranking model for a search feature. Offline evaluation showed a clear improvement in NDCG, but after rollout, click-through rates dropped noticeably in the live experiment.

*Task:* I needed to find the root cause quickly and either fix the model or roll back without losing weeks of work.

*Action:* I started by logging the feature values the model received in production and comparing them to the training distribution. I found that one key feature, session length, was computed differently at training time versus serving time. The training pipeline used padded sessions, but the serving pipeline used raw sessions, creating a distribution mismatch. I fixed the feature pipeline to align both environments, retrained on corrected data, and ran a shadow evaluation before the next rollout.

*Result:* The corrected model matched the offline gains in production. I also added a feature validation check to the training pipeline so the same mismatch would surface as a build error in future runs.

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Q: Tell me about a time you handled cold-start in a recommendation system.

*Situation:* Our platform onboarded a large wave of new users during a promotional campaign, and our existing collaborative filtering model had no history for them.

*Task:* I was responsible for improving recommendations for new users without waiting for several sessions of data to accumulate.

*Action:* I built a hybrid fallback layer that blended three signals: content popularity within a user's stated genre preferences collected at signup, trending titles in the user's region, and a lightweight content-based model using item metadata. I weighted these signals using a linear combination tuned on historical cold-start users whose data had been held out. I also added an exploration budget so the system would surface less obvious titles occasionally, gathering implicit feedback faster.

*Result:* First-week retention for new users improved compared to the prior baseline, confirmed by an internal experiment. The approach was later extended to handle cold-start for newly added content as well.

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Q: Describe a time you collaborated with a product manager to define ML success metrics.

*Situation:* A product manager wanted a 'smarter' autoplay feature but defined success only as 'users watching more.' I was concerned that optimising pure watch time could reduce satisfaction over time.

*Task:* I needed to align on metrics that reflected both engagement and user satisfaction before any model work began.

*Action:* I ran a short analysis showing that watch time alone was not a reliable proxy for satisfaction in our data, specifically for users who quit early after autoplay misfires. I proposed a composite metric combining watch completion rate and explicit positive signals alongside watch time, and I built a simple dashboard so the PM could track all three during the experiment.

*Result:* The PM agreed to the composite metric. During the A/B test, our updated autoplay logic improved completion rate and reduced early quits, even though raw watch time stayed roughly flat. The PM presented the results internally as a model for user-centric ML thinking.

For ML system design questions, work through this structure before jumping to model choice:

1. Clarify the problem: what does success look like for the business, and what does the model need to predict?
2. Discuss data: what signals are available, how is data collected, and what are the quality or bias risks?
3. Define features: which features matter most, and how would you build and validate them?
4. Choose a model family: explain your reasoning based on data size, latency requirements, and interpretability needs.
5. Training and offline evaluation: what metrics will you use, and how do they connect to the business metric?
6. Deployment and serving: batch or real-time, latency targets, fallback logic.
7. Monitoring: data drift, model performance degradation, and alerting strategy.

For behavioural questions, use the STAR structure: Situation, Task, Action, Result. Keep the Situation brief (two to three sentences), spend most of your time on Action, and always close with a concrete Result, even if you cannot share exact numbers.

For coding questions, think out loud from the start, confirm the expected input and output format, handle edge cases explicitly, and discuss time and space complexity after your solution works correctly.

Roku ML interviewers consistently look for a few qualities that candidates who focus only on algorithms tend to miss.

Production mindset. Roku runs a live platform at scale. Interviewers want to see that you think about what happens after the model is deployed: feature drift, latency budgets, rollback plans, and monitoring. An answer that ends at model training will raise flags in a design round.

Business grounding. Roku makes money from advertising and device sales. Interviewers pay attention to whether you connect ML decisions to business outcomes. Saying 'I would optimise NDCG' is weaker than saying 'I would optimise NDCG because it correlates with session length, which drives ad impressions.'

Clear communication. Because ML work touches product, data engineering, and business teams, interviewers probe whether you can explain trade-offs without jargon. Practise framing decisions as 'this approach costs X but gains Y' rather than leading with algorithm names.

Ownership. Candidates report that Roku values engineers who can take a project from problem definition to production without heavy hand-holding. Stories where you drove decisions, rather than just executed them, tend to land well in behavioural rounds.

Spread your preparation across three to four weeks.

Week 1: Roku product and domain

Use the Roku streaming platform for a few days with fresh eyes. Notice how the home screen ranks content, how search works, and how ads appear. Read any public engineering or research material Roku has published. Understand the difference between their streaming OS business and their ad platform business, since both are relevant to ML roles.

Week 2: ML fundamentals and system design

Review core concepts behind recommendation systems, including collaborative filtering, two-tower models, and learning to rank. Practise designing a full ML system end to end, covering data, features, model selection, offline evaluation, deployment, and monitoring. Time yourself to make sure you can cover all stages comfortably in a single design session.

Week 3: Coding and past project stories

Practise coding problems focused on data structures, sorting, and graph traversal, since ML Engineer coding rounds often blend algorithmic thinking with data manipulation. Prepare three to five strong stories from your past work in STAR format, covering at least one of each: improving a model in production, handling messy or biased data, and working across teams to ship a feature.

Week 4: Mock interviews and gap review

Run at least two full mock interviews with a peer or an online practice partner. Review your weak areas from those sessions. Prepare three to five questions to ask the interviewer about Roku's ML infrastructure, team structure, and how success is measured for the role.

Going straight to the model. Many candidates jump to 'I would use a transformer' before discussing data, problem framing, or evaluation. Interviewers at Roku report this as the most common gap in ML system design rounds. Always frame the problem and data before naming a model.

Ignoring the streaming context. Generic answers that could apply to any ML role miss the mark. Ground your examples and designs in streaming-specific signals such as watch history, skip events, content metadata, and ad interaction patterns.

Weak on production and MLOps. If your answer ends at model training, you are covering half the picture. Production deployment, latency, monitoring, and retraining schedules are all in scope for Roku interviews.

Not quantifying results in STAR answers. Answers that end with 'things improved' are forgettable. Even if you cannot share exact numbers, frame results in terms of the metric, the direction, and the experiment or process that confirmed it.

Over-preparing on theory, under-preparing on communication. Candidates who can recite algorithm derivations but struggle to explain why they chose one approach over another tend not to advance past system design rounds.

Not researching Roku's actual products. Interviewers notice when a candidate has never used the Roku platform. A short hands-on session before your interview is worth more than an hour of abstract reading about the company.