AI / ML Engineer (Research)

Responsibilities:
Research and develop direct speech-to-speech modeling pipelines leveraging LLM backbones (e. g., Qwen) and audio encoders/decoders (e. g., Whisper).
Model and evaluate turn-taking, latency handling, and voice activity detection (VAD) mechanisms for real-time conversational AI.
Explore Agentic Reinforcement Training (ART) and self-learning loops for continual improvement of speech models.
Design and experiment with memory-augmented multimodal architectures that allow persistent recall and contextual understanding across interactions.
Develop novel approaches for expressive speech generation, emotion conditioning, and speaker identity preservation.
Collaborate with the Research Director to define and drive long-term AI research roadmaps around autonomy, speech cognition, and agentic intelligence.
Conduct internal benchmarks and contribute to SOTA research in multimodal learning, audio-language alignment, and agent reasoning.
Work closely with MLEs for model training and evaluation, while focusing entirely on research design, datasets, and experimentation.
Requirements:
Experience: 2+ years of applied or academic experience in speech, multimodal, or LLM research.
Education: Bachelor's or Master's in Computer Science, AI, or a related field.
Programming: Strong in Python and scientific computing; experience in JupyterHub environments.
Core Skills:
Deep understanding of LLM architectures, transformers, and multimodal embeddings.
Experience with speech modeling pipelines: ASR, TTS, speech-to-speech, or audio-language models.
Understanding of turn-taking systems, VAD, prosody modeling, and real-time voice synthesis.
Familiarity with self-supervised learning, contrastive representation learning, and agentic reinforcement (ART).
Strong background in dataset curation, experimental design, and model evaluation.
Tools and Ecosystem:
Comfortable using Agno, Pipecat, HuggingFace, and PyTorch.
Familiarity with LangChain, vector stores, and memory systems for agentic research.
Excellent written communication and the ability to clearly articulate research insights.

Experience
2-5 yrs