Software Engineer, AI/ML, ML Infrastructure, Search Ads

Google•Pittsburgh, PA

1d•$147,000 - $210,000

About The Position

Google software engineers develop next-generation technologies that help billions of users connect, explore, and interact with information. Engineers work on projects critical to Google’s needs and may have opportunities to move between teams and projects as the business evolves. The role calls for versatility, leadership, and readiness to take on problems across the full stack. In this position, you will manage project priorities, deadlines, and deliverables, and design, develop, test, deploy, maintain, and enhance software solutions. Within Google Ads, the team applies machine learning and generative AI models, including Gemini, to support a global advertising business. Its work includes protecting users, improving ad quality, and optimizing campaigns for advertiser return on investment, in close collaboration with teams such as Google Research and DeepMind. The role focuses on machine learning infrastructure for Search Ads, including supporting new modeling techniques and products, prototyping infrastructure and tooling, improving infrastructure reliability and usability, and analyzing and improving performance. US pay is $147,000–$210,000, plus a 15% bonus target, equity, and benefits. Individual pay depends on job-related skills, experience, and relevant education or training.

Requirements

Bachelor’s degree or equivalent practical experience.
At least 2 years of software development experience in one or more programming languages.
At least 1 year of end-to-end machine learning experience, such as model deployment, model evaluation, data processing, or debugging.
At least 1 year of experience in speech/audio, reinforcement learning, ML infrastructure, or another ML specialization.
Experience with machine learning systems or large language models (LLMs).
Experience designing system architecture for enterprise computing environments supporting AI/ML workloads.

Nice To Haves

Master’s degree or PhD in Computer Science or a related technical field.
Experience in machine learning modeling or machine learning infrastructure.
Experience designing distributed machine learning systems.
Experience developing accessible technologies.
Experience with Google’s machine learning stack, such as TensorFlow, SAX, GeMax, or TPUs.
Experience in large-scale distributed training and model parallelism.

Responsibilities

Modify training and serving infrastructure to support new modeling techniques and products.
Prototype new infrastructure, automation, workflows, and tooling.
Improve the robustness, reliability, and usability of infrastructure.
Analyze and debug performance, and make improvements.
Collaborate with team members and partners to build systems that integrate across the ecosystem.
Manage project priorities, deadlines, and deliverables.
Design, develop, test, deploy, maintain, and enhance software solutions.