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.

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 high accuracy requirements, while ensuring scalability of GPU infrastructure and improving throughput of ASR service.
• Profiling and debugging ASR runtime performance bottlenecks across different deployment hardware and environments.

What we're looking for

• Possess a Master's in Computer Science, Electrical Engineering or related fields with 3+ years of experience in speech recognition, speech-llm or AI model inference.
• Display knowledge in deep learning and hands-on programming skills in Python, shell scripts, C/C++; familiarity with ML frameworks such as PyTorch and TensorFlow.
• Demonstrate deep understanding of transformer encoder-decoder frameworks for speech recognition, including attention mechanisms, beam search and sequence-to-sequence modeling for end-to-end ASR systems.
• Understand recent advancements in speech foundation models and speech-LLMs that integrate acoustic and linguistic representations, enabling unified modeling for speech understanding and transcription tasks.
• Have experience in optimizing deep learning model inference on NVIDIA GPUs, including profiling and accelerating AI models using CUDA, TensorRT, and mixed-precision computation to achieve low latency, high-throughput performance.
• Have experience developing and tuning custom CUDA kernels, leveraging CUDA Graphs for efficient execution scheduling, and minimizing kernel launch overhead to maximize GPU utilization.
• Be proficient in end-to-end performance analysis, memory optimization, and deployment of largescale ML models on GPU clusters. Experienced with stream management, asynchronous execution, and integrating frameworks such as PyTorch and TensorFlow for real-time inference.

Salary Range or On Target Earnings:

Minimum:
$151,800.00

Maximum:
$332,200.00

In addition to the base salary and/or OTE listed Zoom has a Total Direct Compensation philosophy that takes into consideration; base salary, bonus and equity value.

Note: Starting pay will be based on a number of factors and commensurate with qualifications & experience.

We also have a location based compensation structure; there may be a different range for candidates in this and other locations

At Zoom, we offer a window of at least 5 days for you to apply because we believe in giving you every opportunity. Below is the potential closing date, just in case you want to mark it on your calendar. We look forward to receiving your application!

Anticipated Position Close Date:

07/21/26

Ways of Working
Our structured hybrid approach is centered around our offices and remote work environments. The work style of each role, Hybrid, Remote, or In-Person is indicated in the job description/posting.

Benefits
As part of our award-winning workplace culture and commitment to delivering happiness, our benefits program offers a variety of perks, benefits, and options to help employees maintain their physical, mental, emotional, and financial health; support work-life balance; and contribute to their community in meaningful ways. Click Learn for more information.