Senior Data Scientist

Requirements

• 3+ years of applied or academic experience in speech, multimodal, or LLM research

• Bachelor’s or Master’s in Computer Science, AI, or Electrical Engineering

• Strong in Python and scientific computing, including JupyterHub environments

• Deep understanding of LLMs, transformer architectures, and multimodal embeddings

• Experience in speech modeling pipelines: ASR, TTS, speech-to-speech, or audio-language models

• Knowledge of turn-taking systems, VAD, prosody modeling, and real-time voice synthesis

• Familiarity with self-supervised learning, contrastive learning, and agentic reinforcement (ART)

• Skilled in dataset curation, experimental design, and model evaluation

• Comfortable with tools like Agno, Pipecat, HuggingFace, and PyTorch

• Exposure to LangChain, vector databases, and memory systems for agentic research

• Strong written communication and clarity in presenting research insights

• High research curiosity, independent ownership, and mission-driven mindset

• Currently employed at a product-based organisation

Responsibilities

• Research and develop direct speech-to-speech modeling using LLMs and audio encoders/decoders

• Model and evaluate conversational turn-taking, latency, and VAD for real-time AI

• Explore Agentic Reinforcement Training (ART) and self-learning mechanisms

• Design memory-augmented multimodal architectures for context-aware interactions

• Create expressive speech generation systems with emotion conditioning and speaker preservation

• Contribute to SOTA research in multimodal learning, audio-language alignment, and agentic reasoning

• Define long-term AI research roadmap with the Research Director

• Collaborate with MLEs on model training and evaluation, while leading dataset and experimentation design

Job Details

Location: Hybrid — Mumbai, Bengaluru, Chennai, India (this is optional, incase you're looking for onsite role)

Interview process

• Screening / HR round

• Technical round(s) — coding, system design, ML case studies

• ML / research deep dive

• Final / leadership round