Asana
Work-management platform with a collaborative, editor-based coding interview
Interview in a few hours? A quick sprint for Asana
AI in interviews
No public policy found. Assume no AI assistance in live rounds unless your recruiter says otherwise.
Typical interview loop
How Asana usually runs it for applied AI and LLM engineer roles.
- Focus: Background and motivation
Recruiter screen
A relaxed call about your resume, interests and why Asana, with room to ask about the process.
- Focus: Object-oriented design
Technical phone screen
An hour with an Asana engineer on one technical problem, usually object-oriented design, lighter than the onsite.
- Focus: Working code, clear reasoning
Coding project
A roughly two-hour build in the language and editor you choose, followed by a review of your solution with an engineer.
- Focus: Data structures and algorithms
Algorithms
Data-structure-heavy problems worked through with the interviewer as a partner rather than recalled from memory.
- Focus: Modelling and system design
Design
Model a problem and design a system, judged on clarity, simplicity, scalability, correctness and stated trade-offs.
- Focus: Experience and ownership
Behavioral (senior roles)
A walk through past technical problems and how you handled them, with STAR-style answers suggested.
Topics to prepare for Asana
The topics its loop weighs most, each with what interviewers check and what to study.
- GenAI system designSystem design
- Python for ML interviewsCoding
- Cost and latency engineering for LLM appsSystem design
- Calling LLM APIs in Python: async, streaming, retriesCoding
- RAG vs fine-tuning vs prompting: choosing the approachSystem design
- Design an AI customer-support agentSystem design
- Design enterprise document search / a knowledge assistantSystem design
- Self-attention from scratchTransformers
How it weights your plan
Selecting Asana when you build a plan multiplies each topic’s priority score:
Official prep and resources
Public candidate write-ups
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