Retell AI is hiring: Research Scientist - Audio in San Francisco
Retell AI, San Francisco, CA, United States
ABOUT THE ROLE
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.
KEY 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.
YOU MIGHT THRIVE IF YOU
Strong ML Research Background – You’ve worked on advanced ML problems (e.g., 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.
JOB DETAILS
Cash: $225,000 - $400,000 base salary
Equity: Offers Equity
Location: Redwood City, CA, US (100% Relocation Provided)
US Visas: Retell AI is open to sponsoring work authorization for qualified candidates, including H1B/H-1B, TN, L-1, E-3, F-1 (OPT/CPT), and O-1 visas.
OTHER BENEFITS
100% coverage for medical, dental, and vision insurance
$70/day DoorDash credit for unlimited meals and snacks
$200/month wellness reimbursement
$300/month commuter reimbursement
$75/month phone bill reimbursement
$50/month internet reimbursement
COMPENSATION PHILOSOPHY
Best Offer Upfront: Choose from three cash-equity balance options, no negotiation needed
Top 1% Talent: Above-market pay (top 5 percentile)
High Ownership: Small teams, >$1M revenue/employee, significant equity
Performance‑Based: Offers tied to interview performance, not past salaries
#J-18808-Ljbffr
In Summary: 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 . Your work will directly influence production systems, bridging cutting-edge research with deployment . The role is a research-driven, high-impact role for ML researchers who want to push the boundaries of real-time AI .
En Español: Sobre el papel Este es un rol de alto impacto dirigido a la investigación para los investigadores ML que quieren superar los límites de la IA en tiempo real. Como Ingeniero Fundador de Investigación del Aprendizaje Automático en Retell, se centrará en avanzar las capacidades de modelo para agentes vocales similares al humano que operan en entornos complejos y reales. Explorarás nuevos enfoques en LLM y modelos de audio, diseñará métodos novedosos de evaluación y sistemas prototipos que mejoran el razonamiento, latencia y calidad de conversación. Su trabajo influirá directamente en los sistemas de producción, haciendo un puente entre la investigación avanzada y el despliegue del mundo real. Si está entusiasmado con resolver problemas ML abiertos, experimentar rápidamente y dar forma a cómo piensan y se desempeñan los sistemas vocales de inteligencia artificial, esta es una oportunidad única para hacerlo a gran escala. RESPONSABILIDADES CEY Investigación & Experimentación Explorar y desarrollar nuevas técnicas en LLM y modelos de audio para mejorar el razonamiento, la latencia y la calidad de las conversaciones en sistemas reales. Prototipo Modelo Prototyping Construir e iterar rápido sobre modelos y tuberías experimentales abiertas, convirtiendo ideas de investigación en prototipos de trabajo. Evaluación y marcado Diseñar nuevos problemas de evaluación, conjuntos de datos y métricas para medir el rendimiento en entornos de voz reales complejos y rápidos.
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.
KEY 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.
YOU MIGHT THRIVE IF YOU
Strong ML Research Background – You’ve worked on advanced ML problems (e.g., 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.
JOB DETAILS
Cash: $225,000 - $400,000 base salary
Equity: Offers Equity
Location: Redwood City, CA, US (100% Relocation Provided)
US Visas: Retell AI is open to sponsoring work authorization for qualified candidates, including H1B/H-1B, TN, L-1, E-3, F-1 (OPT/CPT), and O-1 visas.
OTHER BENEFITS
100% coverage for medical, dental, and vision insurance
$70/day DoorDash credit for unlimited meals and snacks
$200/month wellness reimbursement
$300/month commuter reimbursement
$75/month phone bill reimbursement
$50/month internet reimbursement
COMPENSATION PHILOSOPHY
Best Offer Upfront: Choose from three cash-equity balance options, no negotiation needed
Top 1% Talent: Above-market pay (top 5 percentile)
High Ownership: Small teams, >$1M revenue/employee, significant equity
Performance‑Based: Offers tied to interview performance, not past salaries
#J-18808-Ljbffr
In Summary: 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 . Your work will directly influence production systems, bridging cutting-edge research with deployment . The role is a research-driven, high-impact role for ML researchers who want to push the boundaries of real-time AI .
En Español: Sobre el papel Este es un rol de alto impacto dirigido a la investigación para los investigadores ML que quieren superar los límites de la IA en tiempo real. Como Ingeniero Fundador de Investigación del Aprendizaje Automático en Retell, se centrará en avanzar las capacidades de modelo para agentes vocales similares al humano que operan en entornos complejos y reales. Explorarás nuevos enfoques en LLM y modelos de audio, diseñará métodos novedosos de evaluación y sistemas prototipos que mejoran el razonamiento, latencia y calidad de conversación. Su trabajo influirá directamente en los sistemas de producción, haciendo un puente entre la investigación avanzada y el despliegue del mundo real. Si está entusiasmado con resolver problemas ML abiertos, experimentar rápidamente y dar forma a cómo piensan y se desempeñan los sistemas vocales de inteligencia artificial, esta es una oportunidad única para hacerlo a gran escala. RESPONSABILIDADES CEY Investigación & Experimentación Explorar y desarrollar nuevas técnicas en LLM y modelos de audio para mejorar el razonamiento, la latencia y la calidad de las conversaciones en sistemas reales. Prototipo Modelo Prototyping Construir e iterar rápido sobre modelos y tuberías experimentales abiertas, convirtiendo ideas de investigación en prototipos de trabajo. Evaluación y marcado Diseñar nuevos problemas de evaluación, conjuntos de datos y métricas para medir el rendimiento en entornos de voz reales complejos y rápidos.