AI / ML Engineer

We are looking for an AI/ML Engineer with a strong background in LLM fine-tuning, audio language models, and speech-to-speech pipelines. This is a research-focused role exploring new architectures, training methodologies, and agentic learning behaviors. You will work on LLM-backed speech systems to handle full conversational turn-taking, voice activity detection (VAD), and natural prosody.

Responsibilities:

• Research and develop direct speech-to-speech modeling pipelines using LLM backbones (e. g., Qwen) and audio encoders/decoders (e. g., Whisper).
• Model and evaluate turn-taking, latency handling, and VAD for real-time conversational AI.
• Explore Agentic Reinforcement Training (ART) and self-learning loops for continual improvement of speech models.
• Design memory-augmented multimodal architectures for persistent recall and contextual understanding.
• Collaborate with the Research Director to define long-term AI research roadmaps.

Requirements:

• 2+ years of applied or academic experience in speech, multimodal, or LLM research.
• Bachelor's or master's degree in computer science, AI, or a related field.
• Strong Python and scientific computing skills, with experience in JupyterHub environments.
• Deep understanding of LLM architectures, transformers, and multimodal embeddings.
• Experience with speech modeling pipelines, including 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 training (ART).
• Strong background in dataset curation, experimental design, and model evaluation.
• Comfortable using Agno, Pipecat, Hugging Face, and PyTorch.
• Familiarity with LangChain, vector stores, and memory systems for agentic research.
• Excellent written communication skills and the ability to clearly articulate research insights.