Cantina

Machine Learning Engineer - Voice Conversion

Remote (U.S. or Europe)Full timePosted 3 days ago
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About Cantina:

Cantina is a new social platform founded by Sean Parker with the most advanced AI character creator. Our bots are lifelike, social creatures that can interact wherever people are online—across voice, video, and text. Create yourself, imagine someone new, or choose from thousands of characters to share infinitely scalable, personalized content and seamless group chat.

If you’re excited about how AI can shape creativity and social interaction, come help us build what’s next.

 

About the Role:

We’re looking for a Research / ML Engineer to join our Speech Team to build state-of-the-art speech systems end-to-end—from data specs through production inference. You’ll drive the model ↔ data ↔ eval flywheel for VC and adjacent tasks (controllable TTS, voice design and more), partnering closely with research, data, and infra to ship fast, reliable, and cost-aware models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems.

You will thrive in this role if you:

- See research and engineering as two sides of the same coin and enjoy owning work end-to-end.

- Are results-oriented, flexible, and willing to pick up whatever moves the needle.

- Like collaborating closely with infra, data, and product to ship measurable improvements.

- Enjoy designing experiments, listening tests, and metrics that correlate with user-perceived quality.

- Eager to learn every-day, find and solve unique large-scale problems.

What You’ll Do:

- Model Building: Architect, implement, pre-train, fine-tune, and post-train/alignment (e.g., GRPO/DPO) for large-scale speech models.

- Experimental Design: Design, run, and analyze scientific experiments to advance our understanding of the models.

- Tool Development: Develop and improve dev tooling to enhance team productivity.

- Full-Stack Contribution: Contribute to the entire stack, from low-...