Company interview guideReverie Language Technologies
All AI roles · updated 2026.07
“Included as a consequential modern Indian language ai employer with relevant product, engineering, data or AI roles.”
4
stages
2–5 weeks
end to end
20
practice questions
01
The interview, stage by stage
- 1
Founder or hiring manager screen60 min
Indic NLPtranslationPrepare one quantified example demonstrating Indic NLP.
- 2
Practical role exercise60 min
translationcode-switchingPrepare one quantified example demonstrating translation.
- 3
Technical/product deep dive60 min
code-switchingevaluationPrepare one quantified example demonstrating code-switching.
- 4
Team values and ownership round90 min
evaluationdeploymentPrepare one quantified example demonstrating evaluation.
02
What decides the offer
Indic NLP
25%
Shows specific decisions and measurable evidence for Indic NLP.
Translation
20%
Shows specific decisions and measurable evidence for translation.
Code switching
20%
Shows specific decisions and measurable evidence for code-switching.
Evaluation
20%
Shows specific decisions and measurable evidence for evaluation.
Deployment
15%
Shows specific decisions and measurable evidence for deployment.
They look hardest for Indic NLP, translation, code-switching, evaluation.
03
Your four weeks
Days 1–2
Company and market
- ·Map products, users, revenue model and competitors
- ·Write a one-page company thesis
Days 3–5
Core role skills
- ·Practice Indic NLP
- ·Practice translation
- ·Practice code-switching
Days 6–8
Company-context cases
- ·Evaluate translation across low-resource languages.
- ·Handle transliteration and code mixing.
- ·Build language data without leakage.
Days 9–11
Technical simulations
- ·Complete two timed exercises
- ·Run one architecture/product mock
- ·Practice follow-up pressure
Days 12–14
Stories and final loop
- ·Prepare six quantified ownership stories
- ·Rehearse project deep dive
- ·Prepare interviewer questions
04
Practice these
- How would you design an AI assistant for doctors?Recommended
- How would you launch an AI feature whose output cannot always be objectively graded?Recommended
- Design a production RAG system for ten million documents.Recommended
- Offline evaluations improved but production metrics fell. What happened?Recommended
- How do you evaluate outputs when human raters disagree?Recommended
- Compare supervised fine-tuning, preference optimization, and reinforcement learning from feedback.Recommended
- Design an ablation study for a new agent architecture.Recommended
- How should a team set release thresholds when safety metrics have uncertainty?Recommended
- How would you audit whether an AI system treats demographic groups fairly?Recommended
- How would you detect memorization or sensitive-data leakage from a model?Recommended
- How would you build a feedback loop without amplifying user bias or abuse?Recommended
- Design an AI coding assistant for a large enterprise codebase.Recommended
- Design memory for a long-running personal AI assistant.Recommended
- A model refuses too often after a safety update. How do you diagnose and fix it?Recommended
- Evaluate LLMs on a toy task and use LLMs to generate additional evaluation data.Recommended
- Design a language model that minimizes harmful outputs while remaining useful and expressive.Recommended
- Assign human labelers, tasks, and models so every pairing is balanced.Recommended
- Represent a one-nearest-neighbor classifier using a feed-forward neural network.Recommended
- Design a machine-learning system.Recommended
- What approaches would you use to improve transformer efficiency and performance?Recommended
05
Where people slip
- !Generic enthusiasm about startup culture or AI
- !No understanding of the company business model
- !Ignoring Indian price sensitivity, regulation or operational variance
- !Architecture without failure recovery and observability
- !Claiming a recommended case was actually asked
06
Ask them this
- ?What is the exact loop for this team and level?
- ?Which rounds permit AI tools?
- ?What would I own in the first 90 days?
- ?What is the hardest product or model constraint the team faces in India?
- ?How does the team measure quality after launch?
- ?How are ESOPs valued and what is the exercise policy?
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