AI/ML Infrastructure Engineer — Search Ads

Google LLC; Mountain View (CA); On-site; USD 147,000 - 210,000; Full time

Job summary

Google LLC in Mountain View, California is seeking a Software Engineer focusing on AI/ML infrastructure and scalable systems. You will design, build, and optimize platforms that support end-to-end machine learning workloads, including model deployment, evaluation, data processing, and debugging at scale.

We value hands-on experience with ML systems or LLMs, and expertise in enterprise AI/ML architecture. The role involves collaboration across teams to deliver robust pipelines and secure,

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 2 years of software development experience in one or more languages.
• 1 year end-to-end ML experience (deployment, eval, data processing, debugging).
• 1 year experience with speech/audio, RL, ML infra, or related ML field.
• Experience with ML systems or Large Language Models (LLMs).
• Experience designing system architecture for AI/ML workloads.

Responsibilities

• Modify infrastructure (training and serving) to support new modeling techniques and products.
• Prototype new infrastructure, automation, workflows, and tooling.
• Improve robustness, reliability, and usability of infrastructure.
• Analyze and debug performance, and make improvements.
• Collaborate closely with team members and partners to build systems that integrate seamlessly across the ecosystem.

Skills

Software development experience

End-to-end ML experience

ML systems / LLMs experience

AI/ML architecture for enterprise

Speech/Audio or RL or ML infra

Education

Bachelor’s degree or equivalent practical experience

Job description

Google LLC in Mountain View, California is seeking a Software Engineer focusing on AI/ML infrastructure and scalable systems. You will design, build, and optimize platforms that support end-to-end machine learning workloads, including model deployment, evaluation, data processing, and debugging at scale.

We value hands-on experience with ML systems or LLMs, and expertise in enterprise AI/ML architecture. The role involves collaboration across teams to deliver robust pipelines and secure,