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AI Voice Style Transfer Engineer

Daniel Sokolov

Full-time · Job · Los_Angeles

About the role

Voice style transfer sits between voice cloning and text-to-speech: given a target speaker's characteristics — their timbre, pace, prosodic patterns, breathiness, resonance — synthesize speech that carries those stylistic traits without requiring hours of training audio. A brand should be able to define a 'voice identity' from a 3-minute sample and have that identity applied consistently across all their generated content. Our voice platform processes 40 million minutes of synthetic speech per month for media, advertising, and enterprise clients. Style transfer is the next major capability: brands want consistent voice identity across product demos, marketing content, and customer interactions without requiring dedicated voice talent for every piece of content. You'll lead the voice style transfer research and implementation: designing disentangled representation models that separate style from content, training transfer models on our 1,200-hour voice corpus, and implementing the style conditioning interface that lets clients define and apply voice identities. The evaluation challenge is real — perceptual quality assessment requires listening panels, not just objective metrics, and you'll need to build both.

Responsibilities

  • Design disentangled voice representation models separating speaker style from linguistic content
  • Train style transfer models enabling target speaker characteristics from 3-minute reference samples
  • Build style conditioning interface for brand voice identity definition and application
  • Implement perceptual quality evaluation combining listening panel studies and objective metrics
  • Develop style consistency monitoring ensuring voice identity preservation across content types
  • Optimize inference latency for real-time style transfer at production scale

Requirements

  • 6+ years in speech synthesis, voice ML, or audio deep learning research
  • Deep expertise in neural TTS, voice conversion, or speaker representation learning
  • Strong Python and PyTorch skills with audio signal processing experience
  • Familiarity with disentangled representation learning approaches
  • Research publication record in voice AI or speech synthesis preferred

Benefits

  • Hybrid: 2 days LA studio, 3 days remote
  • Top-of-market compensation for voice AI expertise
  • Research publication budget
  • Comprehensive health benefits
  • Equity participation

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Categories

AI & Machine Learning Engineers NLP & Prompt Engineering

Skills

python pytorch hugging face docker gcp

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

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AI Expertise

AI & Machine Learning Engineers NLP & Prompt Engineering

Skills

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