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Multimodal AI Model Optimization Research Engineer
tavus · Remote · Posted 144 days ago
Transform cutting-edge multimodal AI research into production systems, optimizing models for real-time human-like video and audio interactions at scale.
ABOUT US
At Tavus, we're building the human layer of AI. Our mission is to make human-AI interaction as natural as face-to-face interaction, enabling the human touch where it has been previously unscalable.
We achieve this through pioneering research in multimodal AI for modeling human-to-human communication (language, audio, and video), as well as generating audio-visual avatar behavior. Our models power everything from text-to-video AI avatars to real-time conversational video experiences across industries like healthcare, recruiting, sales, and education.
By enabling AI to see, hear, and communicate with human-like authenticity, we're creating the foundation for the next generation of AI employees, assistants, and companions.
We are a Series B company backed by top investors, including Sequoia, Y Combinator, and Scale VC. Join us in driving the future of human-AI interaction.
THE ROLE
We’re looking for an experienced Research Scientist/Engineer with a focus on model optimization to join our core AI team.
Our ideal partner-in-crime thrives in startup environments, is comfortable prioritizing independently, and is willing to take calculated risks. We’re moving fast and looking for people who can help pave the path.
YOUR MISSION
- Take cutting-edge research models and make them fast, efficient, and production-ready using sparsification, distillation, and quantization
- Own the optimization lifecycle for key models: define metrics, run experiments, and benchmark trade-offs across latency, cost, and quality
- Partner closely with researchers and engineers to turn new ideas into deployable systems
REQUIREMENTS
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This posting is sourced from tavus's own career site — applying takes you there directly.
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There are 19 open roles at tavus right now. This role centers on Pytorch, Model Optimization, Knowledge Distillation, and Pruning.
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