Conversation Quality Analyst

This is an internship role for someone early in their career who wants hands-on exposure to how a voice AI company builds trust in its own AI.

What You’ll Do

Annotation

Listen to and annotate real customer calls - transcription review, issue tagging, labeling - using tools like Label Studio

Follow (and help sharpen) annotation guidelines for a multilingual environment (Hindi, English, Hinglish, )

Verifying LLM-as-Judge Evaluations

For calls flagged by our automated eval pipeline, verify whether the model’s call was actually correct - for example, confirming whether a detected barge-in (agent/customer talking over each other) genuinely happened by listening to the audio

Mark agreements and disagreements clearly, with reasoning, so we can measure and improve model accuracy over time

All tools needed for this will be provided

Verifying Quantitative Measures

Check system-flagged quantitative signals against the actual call - e.g., confirming whether an “agent interruption” the system detected really occurred at that timestamp

Flag false positives/negatives so we can tighten detection logic

Help identify edge cases that current rubrics or detection logic don’t handle well

Inspecting Calls & Surfacing New Issues

Regularly inspect calls beyond flagged ones to spot new or emerging issues our rubrics and detection systems don't yet cover

Bring these patterns back to the team so rubrics, prompts, and detection logic keep improving

What We’re Looking For

Must-have

Strong attention to detail and the patience to do focused, high-precision work across many calls

Multilingual comfort preferred - Telugu, Tamil, Kannada, Marathi, Gujarati, or Bengali, in addition to English/Hindi

Comfortable learning new tools quickly - Label Studio, dashboards, internal QA apps

Genuine curiosity about AI and voice AI - you want to understand why a call was flagged, not just complete a checklist

Good to have

Any prior exposure to data annotation, labeling, or QA work

Familiarity with spreadsheets/basic SQL or comfort reading dashboards (e.g., Metabase)

Background in linguistics, call center operations, or content moderation

How This Role Grows

This is designed as an entry point, not an endpoint. Based on where you show strength, we’ll shape what comes next:

Strong rigor and consistency in annotation → QA Lead / Annotation Lead

Curiosity about agent logic and how voice AI actually works → Forward Deployed Engineer track

Strong pattern recognition and rubric thinking → Data/Eval Engineer, Voice AI Analyst

We’ve seen people join in QA and grow into much broader voice AI builder roles - this is meant to be a real foot in the door, not a dead-end task.

Why Bolna

Direct exposure to how a fast-growing AI infra company builds trust in its own models

Real ownership over a function (call quality) that directly affects what customers see

Fastest way to learn the guts of voice AI - ASR, agent logic, eval pipelines - from the ground up

Bengaluru office, in-person team collaboration