Conversation Quality Analyst

About BolnaBolna is Voice AI infrastructure built for India - and now for the world. We help businesses deploy intelligent voice agents that can call, converse, and convert in any language, at scale. From collections to customer support to sales, our agents handle millions of conversations so humans don’t have to.We’re a YC F25 company, backed by General Catalyst, with 1,050+ paying customers and growing fast. Our team of ~25 is based in Bengaluru.The RoleEvery voice AI agent Bolna deploys makes real-time judgment calls - when to speak, when to go silent, when a customer is done talking, when to hand off. We’re building automated systems to grade these calls at scale, using LLMs as judges of call quality. But before you trust a model’s judgment, you verify it against a human’s.That’s this role. You’ll listen to real calls, annotate what actually happened, and check whether our automated systems - LLM-as-judge evals and quantitative signal detection - got it right. It’s precise, high-attention work, and it sits right at the center of how we know our voice agents are actually working.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 DoAnnotationListen to and annotate real customer calls - transcription review, issue tagging, labeling - using tools like Label StudioFollow (and help sharpen) annotation guidelines for a multilingual environment (Hindi, English, Hinglish, )Verifying LLM-as-Judge EvaluationsFor 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 audioMark agreements and disagreements clearly, with reasoning, so we can measure and improve model accuracy over timeAll tools needed for this will be providedVerifying Quantitative MeasuresCheck system-flagged quantitative signals against the actual call - e.g., confirming whether an “agent interruption” the system detected really occurred at that timestampFlag false positives/negatives so we can tighten detection logicHelp identify edge cases that current rubrics or detection logic don’t handle wellInspecting Calls & Surfacing New IssuesRegularly inspect calls beyond flagged ones to spot new or emerging issues our rubrics and detection systems don't yet coverBring these patterns back to the team so rubrics, prompts, and detection logic keep improvingWhat We’re Looking ForMust-haveStrong attention to detail and the patience to do focused, high-precision work across many callsMultilingual comfort preferred - Telugu, Tamil, Kannada, Marathi, Gujarati, or Bengali, in addition to English/HindiComfortable learning new tools quickly - Label Studio, dashboards, internal QA appsGenuine curiosity about AI and voice AI - you want to understand why a call was flagged, not just complete a checklistGood to haveAny prior exposure to data annotation, labeling, or QA workFamiliarity with spreadsheets/basic SQL or comfort reading dashboards (e.g., Metabase)Background in linguistics, call center operations, or content moderationHow This Role GrowsThis 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 LeadCuriosity about agent logic and how voice AI actually works → Forward Deployed Engineer trackStrong pattern recognition and rubric thinking → Data/Eval Engineer, Voice AI AnalystWe’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 BolnaDirect exposure to how a fast-growing AI infra company builds trust in its own modelsReal ownership over a function (call quality) that directly affects what customers seeFastest way to learn the guts of voice AI - ASR, agent logic, eval pipelines - from the ground upBengaluru office, in-person team collaboration