Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
- 1 year of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
- 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
Preferred qualifications:- Master's degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
- Experience with multimedia applications, video processing, or computer vision ML tasks.
- Experience building and scaling ML pipelines on cloud infrastructure (e.g., Google Cloud Platform) and deploying optimized models to edge devices (e.g., mobile, embedded systems).
- Experience with model optimization techniques for performance and latency reduction (e.g., quantization, pruning, hardware-aware tuning).
- Familiarity with AI agent architectures, large language models (LLMs), or orchestration systems (e.g., LangChain, AutoGen).
- Strong understanding of distributed systems, system architecture, and toolchain development for engineering teams.
About the jobOur AI engineering mission is to accelerate multimedia innovation from concept to silicon. We achieve this by building reliable toolchains, rapidly prototyping ML pipelines, and developing agentic multimedia verification workflows that validate our solutions for next-generation chips.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $147000 - $210000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Design and develop robust toolchains to support and accelerate multimedia AI activities.
- Develop specialized AI agents and orchestration architectures to work together seamlessly on complex goals.
- Implement, deploy, and scale multimedia Machine Learning (ML) pipelines across Google Cloud and edge devices.
- Optimize machine learning models for improved performance, latency, and deployment efficiency.
