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AI Text-to-Speech Latency Optimization Engineer

Daniel Sokolov

Full-time · Job · Los_Angeles

About the role

PyTorch, Kubernetes, and GCP are what our voice synthesis infrastructure runs on, serving real-time speech to conversational AI products used across dozens of client applications. The team here reads speech synthesis papers monthly and expects you to bring at least one optimization idea from recent research into a real benchmark within your first quarter. We expect you to reduce end-to-end synthesis latency measurably, working closely with the inference team to ship optimizations that hold up under real production load, not just in a notebook.

Responsibilities

  • Profile and optimize text-to-speech inference latency
  • Implement optimizations from recent speech synthesis research
  • Benchmark changes under realistic production load
  • Collaborate with the inference infrastructure team
  • Document optimization tradeoffs for the wider team

Requirements

  • Strong PyTorch skills and experience with model optimization
  • Familiarity with Kubernetes and GCP-based deployment
  • Ability to read and implement findings from research papers
  • Experience with real-time or low-latency ML systems

Benefits

  • Remote position
  • Health, dental, vision insurance
  • Conference travel budget
  • Equity

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Categories

AI & Machine Learning Engineers MLOps & AI Infrastructure

Skills

python pytorch docker kubernetes gcp

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$160k – $200k / year

AI Expertise

AI & Machine Learning Engineers MLOps & AI Infrastructure

Skills

python pytorch docker kubernetes gcp

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