Section 1: How TikTok Evaluates Machine Learning Engineers in 2026
TikTok’s machine learning interviews are fundamentally shaped by one reality: ML is the product. Unlike companies where ML supports a broader business function, TikTok’s core user experience, content discovery, ranking, monetization, and safety, is almost entirely driven by machine learning systems. This makes TikTok’s ML interviews uniquely demanding, fast-moving, and deeply product-oriented.
By 2026, TikTok has refined its ML hiring philosophy around three pillars: scale, real-time decision-making, and measurable impact. Interviewers are not primarily interested in whether you know ML theory in isolation. They are interested in whether you can build, reason about, and operate ML systems that respond to user behavior in milliseconds, adapt continuously, and influence billions of content impressions every day.
The first thing to understand is that TikTok treats ML engineers as product owners of algorithms. A ranking model is not an experiment, it is a live system shaping what users see, how creators grow, and how revenue is generated. Interviewers therefore probe whether candidates naturally think in terms of feedback loops, incentives, and unintended consequences.
This is where many candidates struggle. Engineers coming from more traditional ML backgrounds often answer questions as if the goal is to maximize offline metrics. TikTok interviewers view that as incomplete. At TikTok, optimizing a metric without understanding its downstream behavioral effects is considered risky.
A defining characteristic of TikTok’s ML interviews is their emphasis on recommendation systems at extreme scale. Candidates are expected to reason about candidate generation, ranking, re-ranking, exploration vs. exploitation, and cold-start behavior, all under strict latency and throughput constraints. Answers that ignore scale or assume unlimited compute are quickly challenged.
Another important distinction is TikTok’s focus on real-time learning. User preferences shift rapidly, trends emerge and die within hours, and models must adapt without destabilizing the platform. Interviewers therefore ask questions that test whether candidates understand online learning, delayed feedback, and the challenges of training on non-stationary data.
This focus on dynamic systems aligns closely with broader ML interview trends where reasoning ability is prioritized over static correctness, as discussed in The Hidden Metrics: How Interviewers Evaluate ML Thinking, Not Just Code. At TikTok, how you think about change is often more important than what you optimize for at a single point in time.
TikTok also places significant weight on content safety and responsibility. Recommendation systems can amplify harmful content just as easily as engaging content. Interviewers therefore probe whether candidates consider moderation signals, policy constraints, and safety tradeoffs as part of ML system design, not as downstream patches.
This is why TikTok ML interviews often blend technical and ethical reasoning. Candidates may be asked how to balance engagement with content quality, or how to detect and suppress harmful trends without overcorrecting. These are not trick questions; they reflect real decisions TikTok ML teams make daily.
Another subtle but critical aspect of TikTok’s interviews is their emphasis on iteration speed. TikTok operates on rapid experiment cycles. Interviewers look for candidates who can design experiments that produce reliable signals quickly, without corrupting user experience or model stability.
Candidates who over-engineer solutions often struggle here. TikTok values pragmatic ML engineering, systems that are good enough, observable, and adaptable, over theoretically optimal but slow-to-iterate approaches.
TikTok’s interviewers also pay close attention to communication style. Because ML decisions directly affect product behavior, ML engineers must clearly explain tradeoffs to product managers, trust & safety teams, and business stakeholders. Interviewers often assess this indirectly by listening to how candidates structure their answers and explain assumptions.
This is why thinking aloud is particularly valuable in TikTok interviews. Candidates who narrate their reasoning, surface uncertainties, and justify decisions tend to perform better than those who jump straight to conclusions. This mirrors patterns discussed in How to Think Aloud in ML Interviews: The Secret to Impressing Every Interviewer.
Finally, TikTok evaluates seniority differently from many companies. Senior ML engineers are not defined by years of experience or paper counts. They are defined by system-level intuition: anticipating feedback loops, understanding how small changes propagate through ranking systems, and knowing when not to optimize.
The purpose of this guide is to help you prepare with that mindset. Each section that follows will break down real TikTok-style ML interview questions, explain why TikTok asks them, show how strong candidates reason through them, and highlight the hidden signals interviewers are listening for.
If you approach TikTok ML interviews like a generic recommendation systems interview, they will feel chaotic. If you approach them as conversations about live systems, incentives, and scale, they become structured and predictable.
Section 2: Recommendation Systems & Ranking Fundamentals (Questions 1–5)
At TikTok, recommendation systems are not a supporting function, they are the product. Interviewers use these questions to evaluate whether you understand ranking systems as live, feedback-driven, real-time decision engines, not static ML pipelines. Candidates who describe recommender systems purely in academic terms often struggle here. Candidates who reason in terms of incentives, latency, and user behavior dynamics perform far better.
1. How would you design a recommendation system for TikTok’s “For You” feed?
Why TikTok asks this
This is the canonical TikTok ML question. It tests system-level thinking, scale awareness, and product intuition.
How strong candidates answer
Strong candidates frame the system as a multi-stage pipeline: candidate generation, ranking, re-ranking, and post-processing. They explicitly discuss latency budgets, explaining that early stages prioritize recall and speed, while later stages focus on precision and personalization.
They also emphasize that the system must continuously adapt based on real-time feedback, likes, watch time, replays, skips, and shares, while avoiding overfitting to short-term noise.
Example
Candidate generation may use user embeddings and content clusters to retrieve thousands of videos, while ranking narrows this to a small, high-quality feed under strict latency constraints.
What interviewers listen for
Whether you naturally discuss staging and latency, not just “the model.”
2. How do you choose ranking objectives for a short-form video platform?
Why TikTok asks this
Optimizing the wrong objective can destroy user trust. This question tests whether you understand metrics as incentives.
How strong candidates answer
Strong candidates explain that no single metric is sufficient. Watch time matters, but so do completion rate, replays, and long-term retention. They discuss combining metrics into composite objectives while monitoring for pathological behavior, such as clickbait or repetitive content.
They also emphasize that objectives must align with long-term user satisfaction, not just immediate engagement.
This mindset aligns with broader interview expectations around business impact, as discussed in Beyond the Model: How to Talk About Business Impact in ML Interviews.
Example
Optimizing pure watch time may promote longer but lower-quality videos that hurt overall experience.
What interviewers listen for
Whether you explicitly mention long-term vs short-term tradeoffs.
3. How do you handle cold-start users and new content in TikTok’s recommender system?
Why TikTok asks this
Cold start is unavoidable at TikTok’s scale. This question tests exploration strategy and fairness.
How strong candidates answer
Strong candidates explain that cold-start problems require deliberate exploration. For users, this may involve lightweight onboarding signals or popular content sampling. For creators, it requires controlled exposure to gather early signals without overwhelming the feed.
Candidates emphasize that exploration must be balanced, too little starves new content, too much degrades user experience.
Example
New videos may be shown to a small, diverse audience slice to collect engagement signals before broader distribution.
What interviewers listen for
Whether you talk about controlled exploration, not random exposure.
4. How do you prevent feedback loops in TikTok’s recommendation system?
Why TikTok asks this
Feedback loops can amplify narrow content and reduce diversity. TikTok uses this question to test systemic thinking.
How strong candidates answer
Strong candidates explain that feedback loops occur when the system over-trusts its own predictions. They discuss introducing exploration, diversity constraints, or regularization to counteract runaway effects.
They also mention monitoring distributional metrics, content diversity, creator exposure, and novelty, to detect unhealthy loops.
This kind of system-level reasoning is closely aligned with how TikTok interviewers evaluate ML thinking, similar to themes explored in The Hidden Metrics: How Interviewers Evaluate ML Thinking, Not Just Code.
Example
A system that only reinforces previously liked content may trap users in narrow interest bubbles.
What interviewers listen for
Whether you anticipate second-order effects.
5. How do you evaluate a recommendation system beyond offline metrics?
Why TikTok asks this
Offline metrics are necessary but insufficient. This question tests whether you understand online evaluation realities.
How strong candidates answer
Strong candidates explain that offline metrics guide development, but online experiments determine success. They discuss A/B testing, guardrail metrics, and careful experiment design to isolate causal impact.
They also emphasize monitoring negative signals, user churn, content fatigue, or creator dissatisfaction, that may not appear in aggregate engagement metrics.
Example
An experiment that increases average watch time but decreases creator diversity may be considered a failure.
What interviewers listen for
Whether you treat offline metrics as inputs, not verdicts.
Why This Section Matters
TikTok interviewers use these questions to assess whether candidates understand recommendation systems as living systems shaped by user behavior, incentives, and scale. Candidates who focus only on model architecture or algorithms often miss the bigger picture. Candidates who reason about pipelines, feedback, and long-term effects stand out.
This section often determines whether interviewers trust you to work on systems that directly shape user experience at TikTok’s scale.
Section 3: Real-Time Learning, Feedback Loops & Experimentation (Questions 6–10)
TikTok operates in an environment where user preferences shift by the minute and content trends can rise and collapse within hours. Interviewers in this section are evaluating whether you understand non-stationary systems, delayed feedback, and the discipline required to experiment safely at massive scale. Candidates who reason as if data is static or experiments are isolated often struggle here.
6. How do you design ML systems that adapt to rapidly changing user preferences?
Why TikTok asks this
TikTok’s relevance depends on freshness. This question tests whether you understand temporal dynamics in recommender systems.
How strong candidates answer
Strong candidates explain that adaptation requires combining short-term and long-term signals. Recent interactions capture transient interests, while longer-term embeddings stabilize preferences. They discuss time-decayed features, session-level modeling, and mechanisms to prevent overreaction to noise.
They also mention that adaptation speed must be controlled, too fast leads to instability; too slow leads to irrelevance.
Example
A user binge-watching cooking videos tonight should see more cooking content now, without permanently rewriting their interests.
What interviewers listen for
Whether you explicitly talk about time horizons and decay, not just “online learning.”
7. How do you handle delayed and implicit feedback in TikTok recommendations?
Why TikTok asks this
Most TikTok feedback is implicit and delayed. This question tests whether you can reason under partial observability.
How strong candidates answer
Strong candidates explain that signals like watch time, replays, and skips arrive asynchronously and are noisy proxies for satisfaction. They discuss modeling techniques that account for delay and uncertainty, and emphasize careful attribution when training models.
They also mention separating learning signals used for ranking from those used for evaluation to avoid leakage.
Example
A video watched to completion may indicate interest, or simply short length, so signals must be normalized.
What interviewers listen for
Whether you treat feedback as probabilistic, not ground truth.
8. How do you prevent runaway feedback loops when models learn in real time?
Why TikTok asks this
Real-time learning can amplify errors quickly. TikTok uses this question to test risk awareness.
How strong candidates answer
Strong candidates explain that feedback loops emerge when the system over-trusts its own predictions. They discuss mechanisms such as exploration quotas, diversity constraints, and delayed incorporation of certain signals.
They also emphasize monitoring distributional metrics to detect narrowing content exposure or creator starvation.
Example
If a trend starts dominating recommendations too aggressively, throttling its exposure can prevent saturation.
What interviewers listen for
Whether you anticipate second-order and compounding effects.
9. How do you design experiments for TikTok’s recommendation systems without harming user experience?
Why TikTok asks this
TikTok runs constant experiments. This question tests experimental rigor and empathy for users.
How strong candidates answer
Strong candidates explain that experiments must be scoped carefully, small initial cohorts, clear guardrails, and fast rollback mechanisms. They discuss choosing metrics that reflect user well-being, not just engagement.
They also mention accounting for interference effects, where one user’s experience influences another’s (e.g., creator exposure).
This thinking aligns with broader ML system design principles discussed in Machine Learning System Design Interview: Crack the Code with InterviewNode.
Example
Testing a new ranking feature on a limited geography reduces risk while collecting signal.
What interviewers listen for
Whether you mention blast-radius control, not just A/B tests.
10. How do you balance exploration and exploitation in TikTok’s feed?
Why TikTok asks this
Exploration is essential for discovery but risky at scale. This question tests tradeoff judgment.
How strong candidates answer
Strong candidates explain that exploration should be intentional and measured. They discuss strategies like contextual bandits, uncertainty-based sampling, or quotas for new content and creators.
They emphasize that exploration is not random, it is guided by hypotheses about what could improve long-term satisfaction.
Example
Introducing a small percentage of novel content into a user’s feed helps discover new interests without degrading experience.
What interviewers listen for
Whether you frame exploration as strategic, not accidental.
Why This Section Matters
TikTok interviewers know that many recommendation failures happen not at model launch, but during continuous learning and experimentation. Candidates who treat ML systems as static pipelines miss this reality. Candidates who reason about time, uncertainty, and compounding effects demonstrate readiness for TikTok’s environment.
This section often determines whether interviewers believe you can safely operate ML systems that evolve in real time.
Section 4: Content Safety, Moderation & Responsible ML (Questions 11–15)
At TikTok’s scale, recommendation systems do more than personalize content, they shape culture, amplify trends, and influence behavior. Interviewers in this section are evaluating whether you can design ML systems that maximize engagement without amplifying harm. Candidates who treat safety as a downstream filter struggle here. Candidates who integrate safety into the core ranking and learning loop perform well.
11. How do you incorporate content safety signals into TikTok’s recommendation system?
Why TikTok asks this
Safety is not optional at TikTok. This question tests whether you understand multi-objective optimization under real constraints.
How strong candidates answer
Strong candidates explain that safety signals must be integrated at multiple stages, candidate generation, ranking, and re-ranking. They discuss hard constraints (e.g., blocking disallowed content) alongside soft penalties that reduce exposure without overcorrecting.
They also emphasize latency: safety checks must be fast and reliable to operate in real time.
Example
Borderline content may remain accessible but be deprioritized in recommendations while additional signals are gathered.
What interviewers listen for
Whether you treat safety as part of ranking, not a post-hoc filter.
12. How do you balance engagement optimization with harm reduction?
Why TikTok asks this
Pure engagement optimization can produce harmful outcomes. This question tests ethical tradeoff reasoning.
How strong candidates answer
Strong candidates explain that engagement metrics are proxies, not goals. They discuss defining guardrail metrics, user complaints, policy violations, creator churn, and ensuring optimization does not violate those boundaries.
They also mention long-term effects: content that drives short-term engagement but increases fatigue or harm may reduce retention over time.
This framing aligns with broader responsible-ML expectations discussed in The New Rules of AI Hiring: How Companies Screen for Responsible ML Practices.
Example
Reducing exposure to sensational but misleading content may slightly lower watch time while improving trust.
What interviewers listen for
Whether you articulate long-term user well-being.
13. How do you detect and respond to emerging harmful trends quickly?
Why TikTok asks this
Trends move fast. TikTok uses this question to test early-warning system design.
How strong candidates answer
Strong candidates describe monitoring distributional shifts, anomaly detection on engagement patterns, and rapid human-in-the-loop review. They emphasize escalation paths and temporary throttling to buy time for investigation.
They also note that false positives are costly; systems must be calibrated to act decisively without panic.
Example
A sudden spike in engagement around a risky challenge may trigger temporary exposure limits pending review.
What interviewers listen for
Whether you design for speed with control, not overreaction.
14. How do you evaluate safety interventions without harming the platform ecosystem?
Why TikTok asks this
Safety changes can affect creators and users unevenly. This question tests ecosystem thinking.
How strong candidates answer
Strong candidates explain that safety interventions should be tested with controlled rollouts and clear success criteria. They discuss measuring collateral impact, creator reach, diversity, and user satisfaction, alongside safety outcomes.
They also emphasize transparency with internal stakeholders to align expectations.
Example
A new moderation classifier may reduce violations but also suppress legitimate niche content if thresholds are miscalibrated.
What interviewers listen for
Whether you measure side effects, not just primary outcomes.
15. How do you prevent bias and unfair exposure in TikTok’s recommendations?
Why TikTok asks this
Algorithmic bias can marginalize creators and communities. TikTok uses this question to test fairness awareness at scale.
How strong candidates answer
Strong candidates explain that bias can enter through data, objectives, and feedback loops. They discuss auditing exposure across creator segments, adjusting exploration policies, and validating fairness metrics over time.
They also emphasize that fairness is contextual and must be revisited as the platform evolves.
Example
Ensuring new creators receive initial exposure prevents entrenched popularity bias.
What interviewers listen for
Whether you treat fairness as continuous monitoring, not a one-time fix.
Why This Section Matters
TikTok interviewers know that recommendation systems can cause harm unintentionally, and quickly. Candidates who optimize engagement without discussing safety tradeoffs are rarely advanced. Candidates who integrate safety, fairness, and responsibility into system design and evaluation demonstrate readiness for TikTok’s real challenges.
This section often determines whether interviewers trust you to work on systems that influence billions of daily interactions.
Section 5: Infrastructure, Scalability & ML Systems at TikTok (Questions 16–20)
TikTok’s machine learning systems operate at a scale where infrastructure decisions are product decisions. Models do not live in isolation; they are deeply embedded in streaming pipelines, real-time feature stores, and globally distributed serving systems. Interviewers in this section are evaluating whether you can reason about ML systems as high-throughput, low-latency production infrastructure, not just training workflows.
16. How do you design ML systems that scale to TikTok’s traffic volumes?
Why TikTok asks this
TikTok serves billions of recommendation decisions daily. This question tests whether you understand scale as a first-order constraint.
How strong candidates answer
Strong candidates explain that scalability begins with architectural choices: stateless serving where possible, efficient feature retrieval, and horizontal scaling. They discuss separating offline training from online inference and minimizing synchronous dependencies in the serving path.
They also emphasize capacity planning and load testing, recognizing that traffic patterns can be bursty and unpredictable.
Example
Decoupling feature computation from ranking inference reduces tail latency during traffic spikes.
What interviewers listen for
Whether you think in terms of throughput and tail latency, not just average performance.
17. How do you manage real-time feature pipelines for TikTok’s recommendation systems?
Why TikTok asks this
Real-time features drive relevance. This question tests data engineering maturity.
How strong candidates answer
Strong candidates describe streaming pipelines that ingest user interactions, apply validation and aggregation, and update feature stores with strict latency guarantees. They emphasize schema discipline, versioning, and backpressure handling.
They also mention monitoring data freshness and consistency, since stale or corrupted features can silently degrade model performance.
Example
Delayed updates to user embeddings can cause recommendations to lag behind real user interests.
What interviewers listen for
Whether you mention data quality and freshness, not just pipelines.
18. How do you ensure reliability and fault tolerance in ML serving systems?
Why TikTok asks this
Failures at TikTok scale are inevitable. This question tests resilience engineering.
How strong candidates answer
Strong candidates explain that ML serving systems must degrade gracefully. They discuss fallback strategies, cached recommendations, simpler models, or heuristic-based rankings, when dependencies fail.
They also emphasize redundancy, health checks, and circuit breakers to prevent cascading failures.
Example
If a personalization service is unavailable, the system may temporarily serve popular or trending content.
What interviewers listen for
Whether you design for failure as a normal condition.
19. How do you monitor and debug ML systems in production at scale?
Why TikTok asks this
At TikTok, subtle issues can affect millions of users quickly. This question tests observability mindset.
How strong candidates answer
Strong candidates describe layered monitoring: infrastructure metrics (latency, error rates), model metrics (prediction distributions), and product metrics (engagement shifts). They emphasize anomaly detection and dashboards that surface deviations early.
They also mention tooling for rapid rollback and controlled experimentation to isolate issues.
Example
A sudden shift in score distributions may indicate feature pipeline corruption rather than a model bug.
What interviewers listen for
Whether you connect technical signals to user impact.
20. How do you balance rapid iteration with system stability at TikTok scale?
Why TikTok asks this
TikTok iterates quickly, but instability is costly. This question tests engineering judgment.
How strong candidates answer
Strong candidates explain that iteration should be gated by risk. High-impact changes require staged rollouts, canaries, and clear rollback plans. Lower-risk optimizations can move faster but still require monitoring.
They emphasize minimizing blast radius and learning quickly without destabilizing the platform.
This balance mirrors broader hiring expectations around ML system maturity, similar to themes discussed in The Hidden Skills ML Interviewers Look For (That Aren’t on the Job Description).
Example
Rolling out a new ranking feature to a small user cohort before global deployment reduces risk.
What interviewers listen for
Whether you demonstrate control alongside speed.
Why This Section Matters
TikTok interviewers know that even the best models fail if the surrounding infrastructure is brittle. Candidates who focus only on algorithms struggle here. Candidates who reason about data flow, resilience, and observability demonstrate readiness to operate at TikTok’s scale.
This section often determines whether interviewers see you as someone who can own ML systems end-to-end, not just contribute models.
Section 6: Career Signals, TikTok-Specific Hiring Criteria & Final Hiring Guidance (Questions 21–25)
By the final stage of TikTok’s ML interview loop, interviewers are no longer assessing whether you understand recommendation systems or scalable infrastructure. They are evaluating whether you can be trusted to own algorithms that directly shape culture, creator livelihoods, and user behavior at global scale. The questions in this section surface judgment, motivation, and alignment with TikTok’s operating reality.
21. What distinguishes senior ML engineers at TikTok from mid-level ones?
Why TikTok asks this
TikTok’s senior ML engineers are not defined by titles or tenure. This question tests whether you understand what seniority actually looks like in a live recommender system environment.
How strong candidates answer
Strong candidates explain that senior ML engineers at TikTok consistently:
- Think in systems, not models
- Anticipate feedback loops and unintended amplification
- Balance engagement with ecosystem health
- Make decisions with incomplete data and tight timelines
They emphasize that seniority is demonstrated by preventing failures, not just shipping wins.
Example
A senior engineer argues against an aggressive ranking change that boosts short-term engagement but risks creator burnout.
What interviewers listen for
Whether you frame seniority as judgment under pressure, not technical flash.
22. How do you decide when not to optimize a recommendation metric?
Why TikTok asks this
Over-optimization is one of the biggest risks at TikTok. This question tests restraint and long-term thinking.
How strong candidates answer
Strong candidates explain that metrics are proxies for value, not value itself. They describe recognizing signs of metric gaming, user fatigue, or ecosystem imbalance, and choosing to pause or reverse optimization accordingly.
They also emphasize the importance of guardrails and qualitative signals alongside quantitative metrics.
Example
Stopping an experiment that improves watch time but increases content repetitiveness.
What interviewers listen for
Whether you explicitly say “sometimes the best move is to stop.”
23. How do you handle ethical concerns or discomfort with a ranking outcome?
Why TikTok asks this
TikTok ML engineers regularly confront ethically complex situations. This question tests moral clarity and communication.
How strong candidates answer
Strong candidates explain that they surface concerns early, ground them in evidence, and engage relevant stakeholders, policy, trust & safety, product, rather than acting unilaterally.
They emphasize that raising concerns is part of the job, not a failure.
Example
Flagging a trend that drives engagement but encourages risky behavior.
What interviewers listen for
Whether you demonstrate ownership and courage, not avoidance.
24. Why do you want to work on ML at TikTok specifically?
Why TikTok asks this
TikTok wants candidates who understand what they are signing up for.
How strong candidates answer
Strong candidates articulate interest in working on live, large-scale systems with immediate impact. They acknowledge the responsibility that comes with shaping attention and culture, and express motivation to improve these systems thoughtfully.
They avoid generic “scale” answers and demonstrate awareness of TikTok’s unique challenges.
Example
Wanting to work where ML decisions have visible, real-world consequences, both positive and risky.
What interviewers listen for
Whether your motivation reflects respect for the platform’s influence.
25. What questions would you ask TikTok interviewers?
Why TikTok asks this
This question reveals priorities and maturity.
How strong candidates answer
Strong candidates ask about:
- How TikTok balances growth with responsibility
- How recommendation changes are evaluated holistically
- How ML teams learn from mistakes at scale
They avoid questions focused solely on velocity, perks, or resume optics.
This curiosity aligns with the qualities TikTok values in ML engineers, similar to patterns discussed in The Hidden Skills ML Interviewers Look For (That Aren’t on the Job Description).
Example
Asking how TikTok detects long-term ecosystem degradation shows system-level thinking.
What interviewers listen for
Whether your questions show long-term ownership mindset.
Conclusion: How to Truly Ace the TikTok ML Interview
TikTok’s ML interviews in 2026 are not about building the most sophisticated model. They are about operating algorithms responsibly in a live, rapidly evolving ecosystem.
Across all six sections of this guide, several themes emerge:
- TikTok evaluates ML engineers as product owners of algorithms
- Metrics are treated as signals, not goals
- Feedback loops and second-order effects matter more than offline scores
- Seniority is inferred from restraint, foresight, and judgment
Candidates who struggle at TikTok often do so because they prepare as if they are interviewing for a static ML role. They focus on accuracy without discussing incentives. They optimize engagement without considering fatigue. They treat safety as a filter rather than a system property.
Candidates who succeed prepare differently. They reason about how models change behavior over time. They anticipate compounding effects. They explain when not to optimize. They demonstrate respect for the scale and influence of TikTok’s systems.
If you approach TikTok ML interviews with that mindset, they become challenging, but fair. You are not being tested on cleverness. You are being evaluated on whether TikTok can trust you to shape one of the world’s most influential recommendation systems responsibly.
