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 Training – Rapidly build and iterate on models and pipelines, turning research ideas into working prototypes. Innovate on paradigms, training methods, and inference.
- 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.
REQUIRED
- Strong ML Research Background – You've worked on advanced ML problems (like LLM pre-training and post-training, transcription model training, TTS, or multimodal systems), either in industry or academia.
- Deep Technical Foundation – Comfortable with PyTorch, model architectures, and the math behind modern machine learning.
- Top Academic Background – Master's degree in CS, ML, AI or related field required; PhD preferred. Equivalent research-level engineering experience also considered.
YOU MIGHT THRIVE IF YOU
- Published or Awarded – First/co-author publications at top-tier venues (NeurIPS, ICML, ICLR, ACL, Interspeech, etc.) or notable competition awards are a strong plus.
- Experimental Mindset – You enjoy exploring open-ended problems and iterating quickly on ideas.
- Bridge 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.