Research Scientist - LLM

Retell Ai · San Francisco Bay Area · FullTime

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ABOUT RETELL AI

Retell AI is using first-principles thinking to reimagine the call center with cutting-edge voice AI. Thousands of companies now use Retell's AI voice agents to handle sales, support, and logistics calls that once required large teams of human agents. Backed by Y Combinator, Alt Capital, and other leading investors, we've scaled to $80M in ARR with a team of 50, up from $5M at the start of 2025, and are now valued at over $1.5B.

Our vision for 2026 is to build a modern CX platform where entire contact centers are powered by AI. Instead of basic automation that needs constant human tuning, we're creating intelligent AI “workers” that act as frontline agents, QA analysts, and managers, continuously executing, monitoring, and improving every customer interaction.

We're growing fast and looking for ambitious builders who want to tackle hard technical problems, move quickly, and have a real impact on one of the fastest-growing voice AI companies in the world.

Let's build the future together.

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ABOUT THE ROLE

Retell AI transforms customer experience with voice AI for enterprises, including customers like CVS/Aetna, American Airlines, Lenovo, and Grab. We have more customer stories than we can tell!

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.

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