This is the whole interview, not just a warmup.
Choose a project you know deeply and can diagram from memory. Start with a high-level architecture diagram, then systematically drill into each component, explaining design choices, trade-offs, and potential improvements. Be prepared to defend any component with metrics and alternatives considered.
Pro tip: Anchor your trade-offs in concrete metrics (latency, throughput, cost) and explicitly state what you optimized for and what you sacrificed. Interviewers at TikTok value data-driven decisions and awareness of scale.
Briefly describe the project's purpose, scale, and your role. This frames the discussion and shows you understand the business impact.
Draw a simple diagram showing major components (e.g., clients, services, data stores) and data flow. Keep it abstract to avoid getting lost in details early.
Pick 2-3 critical components and explain their internal design, technologies used, and why they were chosen over alternatives.
For each major decision, state the trade-off (e.g., consistency vs. availability, latency vs. cost) and justify your choice with data or constraints.
Propose how you would evolve the architecture to handle 10x scale, new features, or reduced costs, showing forward-thinking and awareness of limitations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with REST for the client-facing stuff and gRPC internally, which they seemed fine with.
Start by giving a high-level architecture diagram in words, then trace one or two critical end-to-end flows (e.g., video upload and feed retrieval) through the system. For each flow, explicitly call out the API endpoints, protocols, and whether each hop is synchronous or asynchronous, and justify the choice based on latency, reliability, and scale requirements.
Pro tip: Tie every sync/async decision back to a concrete trade-off (e.g., 'we made feed retrieval synchronous because users expect sub-200ms responses, but video transcoding is async via Kafka to decouple and absorb spikes'). Interviewers at TikTok care about scale and user-perceived latency, so quantify where possible.
Briefly restate the system's purpose and the 2-3 core user journeys you'll trace (e.g., upload, feed, engagement). This anchors the discussion and shows you can prioritize.
List the main APIs (e.g., REST/gRPC endpoints) for each journey, including their inputs, outputs, and the services they touch. Mention protocol choices (HTTP/2, gRPC, WebSocket) and why.
Walk through one or two critical paths step by step, from client to backend services to data stores. Highlight where data is transformed, cached, or queued.
For each hop, state whether it's synchronous or asynchronous and explain the rationale (latency, decoupling, fault tolerance, throughput). Mention the protocols used at each hop.
Conclude with how the design handles failures (retries, idempotency, dead-letter queues) and any trade-offs made between consistency, availability, and latency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the problem and its constraints, then walk through each major design decision, explicitly stating the alternatives you considered and why you rejected them. For each choice, quantify how latency, throughput, and cost influenced the trade-off, and conclude with the impact on the final system.
Pro tip: Quantify trade-offs with concrete numbers (e.g., 'we targeted p99 latency under 200ms, which ruled out a synchronous cross-region call') and acknowledge any residual risks or future improvements to show engineering maturity.
Briefly describe the system's goals and the hard constraints (e.g., latency SLOs, expected QPS, budget) that shaped your design space.
List the key architectural choices you made (e.g., data store, communication pattern, caching strategy) and state the primary reason for each.
For each decision, explain 1-2 alternatives you considered and why they were rejected, tying the rejection to specific constraints like latency, throughput, or cost.
Use metrics or estimates to show how each choice affected latency, throughput, and cost, and how you balanced them.
Conclude with the overall outcome, any residual risks, and what you would do differently or improve next time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system's scope and key user flows, then walk through a structured capacity planning process: estimate QPS from DAU and usage patterns, calculate bandwidth from payload sizes, and determine compute needs based on processing per request. Quantify each constraint with realistic numbers and explain how you'd validate and adjust the plan as the system scales.
Pro tip: Always state your assumptions explicitly and show the math—interviewers care more about your reasoning than exact numbers. Also, mention how you'd monitor actual usage and iterate on the plan, demonstrating a data-driven and adaptive mindset.
Ask clarifying questions about the system's features, user base, and growth expectations. State your assumptions (e.g., DAU, peak-to-average ratio) to ground the discussion.
Calculate average and peak QPS from DAU, requests per user per day, and peak traffic multiplier. Break down by read/write and critical endpoints.
Estimate average and peak bandwidth by multiplying QPS by average payload size (request + response). Consider data transfer for different media types (e.g., video, images).
Estimate CPU, memory, and storage needs based on processing per request, service time, and concurrency. Use Little's Law or similar to size server fleet.
Highlight the most constrained resource (e.g., database QPS, network bandwidth) and propose scaling techniques (sharding, caching, CDN, autoscaling). Discuss trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where they spent the most time with me.
Start by framing partial failures as expected in distributed systems, then walk through each mechanism (replication, circuit breakers, backpressure, idempotency, retry policies, dead-letter queues) in the context of a concrete system you've built or TikTok's architecture. For each, explain the problem it solves, how you implemented it, and trade-offs, emphasizing how they work together to maintain availability and data integrity.
Pro tip: Tie each mechanism to a real incident or metric (e.g., 'Our circuit breaker reduced cascading failures by 40% during peak traffic') to show practical impact, and mention how you'd monitor and tune these mechanisms over time.
Briefly describe a system you've worked on and why partial failures are inevitable, setting the stage for the mechanisms you'll discuss.
Discuss how replication ensures data durability and availability, and how idempotent operations prevent duplicate side effects during retries.
Describe how circuit breakers prevent cascading failures by failing fast, and how backpressure protects services from being overwhelmed by load.
Explain your retry strategy (exponential backoff, jitter, max attempts) and how DLQs capture failed messages for later analysis and reprocessing.
Show how these mechanisms work together, and highlight trade-offs like latency vs. consistency, and how you monitor and adjust them.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through distributed tracing across async boundaries, which they seemed interested in.
Start by outlining your observability stack for synchronous (e.g., HTTP/gRPC) and asynchronous (e.g., message queues, event streams) communication, highlighting tools for metrics, tracing, and logging. Then, describe the SLOs you defined for critical user journeys, how you measured them, and your process for managing error budgets, including policies for when budgets are exhausted.
Pro tip: Emphasize how you aligned SLOs with business impact and used error budgets to balance feature velocity and reliability, showing you understand the trade-offs at scale.
Explain the tools and instrumentation used for synchronous calls (e.g., distributed tracing with OpenTelemetry, metrics with Prometheus) and asynchronous flows (e.g., queue monitoring, consumer lag, dead-letter queues).
List specific SLOs (e.g., availability, latency, throughput) for key user-facing operations, ensuring they are measurable and tied to user experience.
Describe how you collect and aggregate data to compute SLO compliance, including alerting on burn rates and dashboards for visibility.
Explain your error budget policy: how you calculate remaining budget, what actions trigger when budget is low (e.g., freeze deployments, prioritize reliability work), and how you communicate status.
Share how you review incidents and adjust SLOs/error budgets over time, fostering a culture of continuous improvement and blameless postmortems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start with a high-level overview of the system and its security requirements, then dive into each area (authentication, authorization, secrets management, PII) with specific technologies and design choices. Emphasize trade-offs and how you balanced security with usability and performance, especially in a large-scale environment like TikTok.
Pro tip: Demonstrate awareness of TikTok's scale and global compliance requirements (e.g., GDPR, CCPA) by mentioning how your solutions handle millions of users and data residency. Also, discuss how you'd evolve the security model over time with threat modeling and regular audits.
Briefly describe the system, its scale, and the security requirements (e.g., user data sensitivity, regulatory compliance). This sets the stage for your design decisions.
Explain how users and services authenticate (e.g., OAuth 2.0, JWT, MFA, SSO). Mention token management, session handling, and protection against common attacks like credential stuffing.
Describe how you enforce access control (e.g., RBAC, ABAC, OAuth scopes). Discuss how permissions are managed and checked at scale, and how you handle least privilege.
Detail how you store and rotate secrets (e.g., HashiCorp Vault, AWS Secrets Manager, KMS). Include how services access secrets securely and how you avoid hardcoding.
Explain how you identify, classify, and protect PII (e.g., encryption at rest/in transit, tokenization, data masking). Discuss retention policies and compliance with regulations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Choose a specific incident where you had clear ownership and can articulate the full lifecycle from detection to resolution. Structure your answer as a narrative that follows the STAR method, emphasizing the technical depth of your root cause analysis and the systemic improvements you implemented. Highlight how you balanced immediate mitigation with long-term prevention, and quantify impact where possible.
Pro tip: Focus on the detection and root cause analysis phases—interviewers at TikTok value engineers who can quickly identify and fix issues at scale. Show how you used data and monitoring to pinpoint the problem, and always mention the permanent fix that prevented recurrence.
Briefly describe the system, your role, and the incident's impact (e.g., user-facing errors, latency spike, data inconsistency). Keep it concise to save time for the technical details.
Explain what was observed (e.g., alerts, dashboards, user reports) and how you detected it. Mention specific monitoring tools or metrics that triggered the investigation.
Detail the investigative steps you took to identify the root cause, including any hypotheses, experiments, or data analysis. Be clear about the underlying technical issue.
Describe the immediate actions taken to mitigate the incident (e.g., rollback, hotfix) and the permanent solution implemented to prevent recurrence (e.g., code refactor, infrastructure change).
Conclude with the lessons learned, any process improvements, and the measurable impact of your fix (e.g., reduced error rate, improved latency).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I had latency numbers and an availability improvement stat.
Pick one or two high-impact projects and lead with the headline metric (e.g., 'reduced p99 latency by 40%'), then briefly explain the baseline, your specific contribution, and how you measured it. Quantify across multiple dimensions (latency, reliability, quality, cost) and tie each number to user or business impact.
Pro tip: Always state the measurement window and methodology (e.g., 'over a 30-day period using production A/B metrics') to make your numbers credible, and if you don't have exact figures, give a defensible range and explain how you'd measure it.
State the starting metrics before your work (e.g., p99 latency was 500ms, error rate 2%) so the improvement has context.
Clarify what you personally did (e.g., redesigned caching layer, optimized queries) to avoid taking credit for team-wide results.
Give concrete numbers for latency, reliability, quality, and cost, and specify the measurement period and method.
Translate technical metrics into user or business outcomes (e.g., 'reduced cart abandonment by 5%' or 'saved $200K annually').
Briefly mention any trade-offs (e.g., increased memory usage) and how you balanced them, showing engineering maturity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Said I'd have started with a simpler data model and avoided a schema migration we had to do six months in.
Choose a specific system you worked on and identify one or two concrete design decisions you would change, explaining the trade-offs and the impact of the redesign. Focus on demonstrating your ability to learn from experience and apply engineering judgment, rather than just listing flaws.
Pro tip: Frame your redesign as a learning opportunity: acknowledge the original constraints and show how your proposed changes would better handle scale, maintainability, or evolving requirements. This shows maturity and a growth mindset.
Briefly describe the system, its purpose, and the constraints under which it was built (e.g., time, scale, team size). This shows you understand the bigger picture.
Select one or two specific aspects you would redesign, such as architecture, data model, API design, or deployment strategy. Avoid broad generalizations.
Discuss why the original design was suboptimal: what problems it caused (e.g., scalability bottlenecks, technical debt, poor developer experience).
Describe your alternative approach in detail, including the technologies or patterns you would use and how they address the issues.
Compare the pros and cons of your redesign versus the original, and quantify the expected benefits (e.g., reduced latency, easier maintenance).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.