Karya

All AI roles · updated 2026.07

Limited public evidence

“Included as a consequential modern Indian ai data employer with relevant product, engineering, data or AI roles.”

stages

2–5 weeks

end to end

practice questions

Hiring now

Karya has 13 open roles right now

landed applies to roles like these for you, on each company’s own site, with answers written for the role.

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Research Associate - AI Evaluations
Bengaluru
Research Engineering Intern, AI Evaluations
Bengaluru, India
Partnerships Associate
Bengaluru, India

Or practice this loop first

The interview, stage by stage

1
Founder or hiring manager screen60 min
data qualityannotation
Prepare one quantified example demonstrating data quality.
2
Practical role exercise60 min
annotationfair work design
Prepare one quantified example demonstrating annotation.
3
Technical/product deep dive60 min
fair work designmultilingual coverage
Prepare one quantified example demonstrating fair work design.
4
Team values and ownership round90 min
multilingual coverageevaluation
Prepare one quantified example demonstrating multilingual coverage.

What decides the offer

Data quality

25%

Shows specific decisions and measurable evidence for data quality.

Annotation

20%

Shows specific decisions and measurable evidence for annotation.

Fair work design

20%

Shows specific decisions and measurable evidence for fair work design.

Multilingual coverage

20%

Shows specific decisions and measurable evidence for multilingual coverage.

Evaluation

15%

Shows specific decisions and measurable evidence for evaluation.

They look hardest for data quality, annotation, fair work design, multilingual coverage.

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 data quality
·Practice annotation
·Practice fair work design

Days 6–8

Company-context cases

·Design a high-quality Indic-language data pipeline.
·Measure annotator agreement and bias.
·Use active learning without narrowing coverage.

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

Practice these

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

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?