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.