AI Scientist – Conversational & Voice Intelligence

About Zudu AI

human-like voice automation

Role Overview

AI Scientist

Key Responsibilities

• Research & Development

• Develop and fine-tune speech recognition (ASR)

  ,

  text-to-speech (TTS)

  , and

  natural language understanding (NLU)

  models.
• Build

  multi-turn conversational AI systems

  capable of contextual reasoning, grounding, and emotional intelligence.
• Explore and implement

  retrieval-augmented generation (RAG)

  and

  memory-based dialogue systems

  for long-term contextuality.
• Research

  prosody, emotion, and style transfer

  in speech synthesis for natural human-like delivery.
• Evaluate and integrate open-source models (e.g., Whisper, Bark, FastSpeech, VITS, GPT-family models).

• System & Data Integration

• Work closely with platform engineers to deploy models in low-latency, production-grade

  environments.
• Optimize

  inference performance

  on cloud and edge systems using quantization, distillation, and caching strategies.
• Collaborate with the voice pipeline team to align model outputs with

  telephony and CPaaS audio protocols

  .

• Experimentation & Evaluation

• Design and conduct experiments to benchmark accuracy, naturalness, latency, and engagement

  .
• Lead

  data annotation and synthetic voice data generation

  projects to improve training quality.
• Publish findings internally (and externally when possible) to maintain Zudu AI’s leadership in enterprise voice automation.

Preferred Qualifications

• Master’s or Ph.D. in

  Computer Science, AI, Speech Processing, or Computational Linguistics

  .
• Strong background in

  deep learning

  ,

  transformer architectures

  , and

  reinforcement learning

  .
• Hands-on experience with

  PyTorch

  ,

  TensorFlow

  , or

  JAX

  .
• Expertise in one or more of the following areas:
• Automatic Speech Recognition (ASR)
• Text-to-Speech (TTS)
• Natural Language Processing (NLP)
• Multimodal or Emotion-aware AI
• Understanding of

  LLM fine-tuning

  ,

  prompt engineering

  , and

  retrieval systems (RAG, FAISS, Milvus)

  .
• Experience deploying models with

  Kubernetes

  ,

  TorchServe

  , or

  ONNX Runtime

  .
• Familiarity with

  speech datasets

  ,

  phoneme-level modeling

  , and

  accent adaptation

  .

Nice-to-Have

• Prior experience in

  telephony or conversational AI products

  .
• Contributions to

  open-source speech or NLP projects

  .
• Understanding of

  low-latency voice streaming pipelines

  (e.g., gRPC, WebRTC).
• Exposure to

  emotion detection

  ,

  sentiment analysis

  , or

  paralinguistic research

  .

Soft Skills

• Curiosity and scientific rigor.
• Ability to translate research into

  deployable, scalable solutions

  .
• Collaborative mindset — able to work with engineers, product leads, and linguists.
• Excellent written and verbal communication.