AI / ML Engineer

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:

• 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 for 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, Hugging Face, and PyTorch.
• Familiarity with LangChain, vector stores, and memory systems for agentic research.
• Excellent written communication and ability to clearly articulate research insights