Zoom Communications

AI Inference Engineer - Speech

San Jose, CA, US$151,800-$332,200Posted 1 day ago

Job Description

  • San Jose, California, United States
  • Seattle, Washington, United States
  • Full time

What you can expect

We are looking for an AI Inference Engineer with a solid background in speech recognition and model inference. In this role, you will develop state-of-the-art automatic speech recognition system and ship it to various Zoom products. You will work on the most cutting edge speech modeling and inference technologies with world-class speech scientists. This role will include collaboration with cross-functional teams, including product, science engineering teams, and infrastructure teams, to deliver high-impact projects from the ground up.

About the Team

Zoom's AI Speech Team is developing speech recognition technologies to improve Zoom's conversational AI experience. This work impacts various products, like Zoom AI Companion, Zoom Meetings and Workplace, Zoom Contact Center, Zoom Phone, Zoom Revenue Accelerator, etc. Our team's mission is to equip the powerful AI brain with human-level listening and understanding undefined for voice input.

As an AI Inference Engineer, you will develop novel speech model inference solutions on modern AI inference hardware, such as GPU, TPU and AI-specific chips. Our goal is to deliver the most unique AI-powered collaboration platform to users across the globe.

Responsibilities

  • Developing state-of-the-art speech services for Zoom products. Devising novel techniques where off-the-shelf solutions are not available.
  • Optimizing ASR inference systems for production deployment, including inference latency, throughput, memory footprint, and resource utilization.
  • Optimizing model inference performance by diving deep into the lower stack of inference frameworks, with a focus on hardware-specific optimizations for Nvidia GPUs.
  • Proposing new model structures by joint optimization of model accuracy and inference speed.
  • Designing and developing ASR systems with low latency and hi

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