Expect a balanced set of coding, design, product, and behavioral questions. For senior and staff roles, systems rigor and cross-functional leadership are emphasized; for AI roles, applied orchestration and evaluation depth matter.

Coding / Algorithms

Focus on correctness, complexity, and communication while handling edge cases.

• Implement a task scheduler that respects dependency ordering and detects cycles.
• Design a data structure for rate limiting with sliding windows.
• Merge k sorted streams efficiently; discuss tradeoffs for memory and latency.
• Implement an LRU or LFU cache and explain eviction semantics.
• Given a large log stream, compute rolling aggregates under memory constraints.

System Design / Architecture

Demonstrate pragmatic scaling, reliability, and observability.

• Design an audit logging platform with retention, search, and export at scale.
• Build a notifications system supporting user preferences and multi-channel delivery.
• Architect a background job framework with retries, idempotency, and tracing.
• Design a multi-tenant export/import system with privacy and compliance guarantees.
• Evolve a monolith toward well-bounded services without disrupting velocity.

Product & Data Modeling

Translate ambiguous requirements into robust models and incremental milestones.

• Model enterprise admin permissions for org-, team-, and project-level controls.
• Design data lifecycle: trash, archive, restore, and hard delete with auditability.
• Propose an MVP for a cross-functional feature; define success metrics and guardrails.
• Outline an A/B experiment for a new onboarding flow; discuss metrics and variants.
• Handle billing/licensing changes while preserving reliability and cost control.

Behavioral / Leadership

Show ownership, collaboration, and clarity under pressure.

• Tell me about a high-severity incident you led—what changed afterward?
• Describe a time you aligned stakeholders with conflicting priorities.
• How do you raise engineering quality on your team?
• Discuss a decision where you traded perfect architecture for delivery speed.
• How do you mentor peers while maintaining throughput?

Reliability & Operations

Highlight instrumentation-first thinking and safe delivery practices.

• Define SLIs/SLOs for an async processing pipeline and your alerting strategy.
• Plan a zero-downtime migration; identify rollback triggers and blast-radius limits.
• Reduce p95 latency by 30%—diagnostics, experiments, and verification.
• Establish guardrails for a new external integration or API.
• Create dashboards to detect regressions and capacity issues early.

Applied AI (role-dependent)

Connect agentic design to evaluation, safety, and performance.

• Design an evaluation harness for multi-step agent tasks; define pass/fail.
• Add a new tool to an agent’s toolbox; model schemas and error handling.
• Propose a memory strategy balancing context, privacy, and cost.
• Route between specialized sub-agents while preserving context and security.
• Diagnose reliability issues: hallucination, tool failures, or prompt regression.

Systems Design & Architecture

We evaluate how you design reliable, scalable, and observable systems. You’ll model data, APIs, storage/indexing, caching, background processing, and failure handling, with emphasis on pragmatic tradeoffs and incremental delivery.

• Be ready to go over:
  • Service boundaries and data modeling: entities, relationships, indexing strategies, consistency needs
  • Traffic patterns & scaling: partitioning, caching layers, back-pressure, queueing, async jobs
  • Reliability & observability: SLIs/SLOs, metrics, logs/traces, retries, idempotency, circuit breakers
  • Advanced concepts (less common): multi-region architecture, schema evolution, rate limiting at scale, API platform considerations
• Example questions or scenarios:
  • “Design an audit logging system with retention, search, and export for enterprise customers.”
  • “Architect a notifications pipeline supporting fan-out, user preferences, and delivery guarantees.”
  • “Scale an async job framework for high-throughput background processing and debuggability.”

Product Thinking & Data Modeling

As a product-led company, we look for engineers who align technical choices with user outcomes. You’ll translate product requirements into data models, UX-aware APIs, and iterative milestones; you’ll also consider experimentation and measurement.

• Be ready to go over:
  • Workflows and user stories: mapping features to entities, permissions, and lifecycle states
  • Data modeling: normalization vs. denormalization, schema evolution, soft delete/archival, exports
  • Experimentation and impact: A/B testing design, metrics selection, guardrail metrics, rollout strategies
  • Advanced concepts (less common): multi-tenant isolation, enterprise admin controls, billing/licensing data flows
• Example questions or scenarios:
  • “Model enterprise admin controls for managing org-wide permissions and auditability.”
  • “Design exports and recovery flows for large datasets with compliance constraints.”
  • “Propose an MVP → GA plan for a new cross-functional feature; define success metrics.”

Collaboration, Leadership & Culture

We value engineers who lead through clarity, empathy, and ownership. Interviews probe how you influence outcomes, resolve ambiguity, and raise the bar for engineering quality.

• Be ready to go over:
  • Cross-functional alignment: partnering with PM/Design/Research, writing clear design docs, resolving tradeoffs
  • Ownership & reliability: on-call maturity, incident response, postmortems, long-term fixes
  • Team impact: mentoring, code reviews, evolving standards, creating leverage for others
  • Advanced concepts (less common): leading multi-quarter initiatives, shaping area roadmaps
• Example questions or scenarios:
  • “Tell us about a time you changed course based on data or customer feedback.”
  • “Describe a high-severity incident you owned—what you did in the moment and how you prevented recurrence.”
  • “How do you mentor peers while maintaining delivery velocity?”

Reliability, Observability, and Operational Excellence

Our customers run mission-critical workflows on Asana. We expect engineers to design for safety, performance, and cost from day one, across product and infrastructure.

• Be ready to go over:
  • SLIs/SLOs and error budgets: defining, monitoring, and acting on them
  • Instrumentation: metrics, tracing, structured logs; debugging at scale
  • Release engineering & CI/CD: rolling deploys, canaries, feature flags, automated health checks
  • Advanced concepts (less common): guardrails for AI systems, eval harnesses, multi-tenant noise isolation
• Example questions or scenarios:
  • “Design observability for an async workflow engine—what signals, dashboards, and alerts?”
  • “Plan a migration that preserves availability and limits blast radius.”
  • “Reduce p95 latency by 30%—what’s your plan and how do you validate impact?”

Applied AI & Agentic Systems (role-dependent)

For AI Teammates roles, we assess your ability to build reliable agent workflows, integrate tools, and measure quality and safety. Prior production agents are not required; we care about fundamentals and your learning velocity.

• Be ready to go over:
  • Agentic workflows: function calling, multi-step planning, memory/state, orchestration
  • Evaluation & safety: offline/online evals, regression tests, guardrails, hallucination controls, cost/latency tradeoffs
  • Grounding & integrations: RAG, connectors (calendars, docs, tickets), auth and permissioning
  • Advanced concepts (less common): multi-agent collaboration, context routing, persistent organizational memory
• Example questions or scenarios:
  • “Design an evaluation harness that measures task success, latency, and cost across prompts/tools.”
  • “Propose a memory strategy for multi-turn workflows with privacy and retention constraints.”
  • “Add a new tool integration; walk through auth, schema, and safety checks.”