Voice agents fail in ways traditional software doesn't. An ASR confidence drop on a regional accent misfires a tool call, an LLM hallucinates a policy because upstream latency broke turn-taking, and support teams roll these agents back within a week without anyone able to explain what went wrong.
What you'll work on
Evaluation frameworks. Text-only evals miss most of what matters in voice: barge-in, prosody, latency-induced errors, cross-turn context loss. You'll design audio-native metrics, generate adversarial conversational datasets across accents and edge cases, and build LLM-as-judge rubrics for task completion, empathy, and recovery from tool failures.
End-to-end observability. Tracing a failed interaction means correlating audio packets, STT hypotheses, LLM reasoning traces, tool calls, and TTS output back to a single conversation ID. You'll help shape the schema and analysis layer that makes cascade failures visible across the stack.
Self-improvement systems. Once you can measure and trace, the interesting work is closing the loop: mining production traces for failure patterns, generating targeted fine-tuning data or prompt updates, and validating that fixes hold under adversarial replay.
Who we're looking for
Someone who cares about the research questions for their own sake, and equally cares whether the work ships. Papers at Interspeech, ACL, NeurIPS, or EMNLP on speech, dialogue systems, agent evaluation, or human-AI interaction are directly relevant.
Comfortable in Python, and familiar with at least one of: speech models (Whisper, Conformer variants), LLM tool-use and agent frameworks, or observability stacks (OpenTelemetry, Langfuse, Arize, Hamming).
Current PhD students in ML, NLP, or speech are the strong default; exceptional MS students or research engineers with a publication track record are welcome to apply.
Nice to have
Prior work on evaluation methodology, dataset synthesis, or interpretability. Experience with real-time systems, telephony, or streaming pipelines. A blog, repo, or workshop paper that shows how you think in public.
This is a full-time role with a competitive salary and ESOPs.
We're building self-healing voice agents for enterprise customer support within ixigo. The system has to know when it's failing, why it's failing, and how to fix itself before a human notices. This fellowship sits at the intelligence layer behind that work.
Our Culture: ixigo is proud to have built an entrepreneurial culture that has become a folk-lore in the startup ecosystem. One in every four ixigems has gone on to build successful startups and companies. Our cultural values of integrity, empathy, ingenuity, awesomeness, and resilience have stood the tests of time and we’ve built a fun, flexible and creative work environment that is driven by people with a high degree of ownership. You will get to work with some of the smartest folks in the Indian startup ecosystem, and solve some of the toughest problems for the next billion users by using bleeding-edge technologies. Oh, and we have an awesome “play” area, great chai/coffee, free lunches (yes, they exist!) and a workspace you will fall in love with.