Research Scientist - LLM

Retell AI•Redwood City, CA

1d•$225,000 - $400,000•Onsite

About The Position

This is a research-driven, high-impact role for ML researchers who want to push the boundaries of real-time AI. As a Founding Machine Learning Research Engineer at Retell, you’ll focus on advancing model capabilities for human-like voice agents operating in complex, real-world environments. You’ll explore new approaches across LLMs and audio models, design novel evaluation methods, and prototype systems that improve reasoning, latency, and conversational quality. Your work will directly influence production systems, bridging cutting-edge research with real-world deployment. If you’re excited about solving open-ended ML problems, experimenting rapidly, and shaping how voice AI systems think and perform, this is a unique opportunity to do so at scale.

Requirements

Strong ML Research Background – You’ve worked on advanced ML problems (for example: LLM pre-training and post training, transcription model training, text to speech model training, or multimodal systems), either in industry or academia.
Deep Technical Foundation – Comfortable with PyTorch, model architectures, and the math behind modern machine learning.
Experimental Mindset – You enjoy exploring open-ended problems and iterating quickly on ideas.
Bridging Theory & Practice – You can translate research into systems that work in real-world environments.
Startup-Ready – You thrive in fast-paced environments with high ownership and ambiguity.
Collaborative & Clear Communicator – You can explain complex ideas and work cross-functionally to drive impact.

Responsibilities

Research & Experimentation – Explore and develop new techniques across LLMs and audio models to improve reasoning, latency, and conversational quality in real-time systems.
Model Prototyping – Rapidly build and iterate on experimental models and pipelines, turning research ideas into working prototypes.
Evaluation & Benchmarking – Design novel evaluation frameworks, datasets, and metrics to measure performance on complex, real-world voice tasks.
Bridge Research to Production – Collaborate closely with engineering to translate research insights into deployable systems.
Human Feedback Loops – Develop methods to incorporate human evaluation into model improvement, especially for subjective conversational quality.
Advance the Frontier – Stay at the cutting edge of ML research and bring new ideas into Retell’s product and infrastructure.