Machine Learning Engineer - Voice Conversion
Quick Summary
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,
Design automated objective/subjective evaluations—listening tests, SV/WER/ASR-based metrics, robustness & bias checks, and red-team studies.
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
~1 min readWe’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.
Responsibilities
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Exceptional research/development experience with large-scale audio models (>8B parameters, >500k hours of data).
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Deep hands-on experience with diffusion and/or flow-matching transformers, including practical knowledge of samplers, schedules, conditioning mechanisms, and distillation.
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Deep hands-on experience training audio VAEs, neural audio codecs, and vocoders latent/tokenizer design, reconstruction and perceptual objectives, adversarial training.
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Strong experience with multi-node, multi-GPU distributed training (FSDP/DeepSpeed or equivalent).
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Strong software engineering skills with a proven track record of building complex systems.
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Strong with PyTorch and performance work (profiling, CUDA/Triton/C++ as needed) and writing reliable production-quality code.
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Shipped large-scale speech/audio or multimodal generative models to production.
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Background in working with large-scale ML data, and the ability to iterate on data and triangulate quality using both subjective and objective signals.
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Experience with voice cloning, speech control/steerability, or expressive speech generation.
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Notable publications and/or open-source contributions in speech/audio/ML.
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What We Offer
~1 min readThe anticipated annual base salary range for this role is between $200,000-$220,000 (€170,000-€190,000). When determining compensation, a number of factors will be considered, including skills, experience, job scope, location, and competitive compensation market data.
Location & Eligibility
Listing Details
- Posted
- August 4, 2026
- First seen
- August 5, 2026
- Last seen
- August 5, 2026
Posting Health
- Days active
- -1
- Repost count
- 0
- Trust Level
- 61%
- Scored at
- August 5, 2026
Signal breakdown
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