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

Google•New York, NY

2d•$207,000 - $300,000

About The Position

Google software engineers develop technologies used at massive scale, spanning areas such as information retrieval, distributed computing, large-scale system design, data storage, artificial intelligence, and natural language processing. In this role, you will work on projects important to Google, manage priorities, deadlines, and deliverables, and design, develop, test, deploy, maintain, and enhance software solutions. Within Google Ads, you will work on AI and machine learning infrastructure and modeling, supporting technologies such as Gemini to help protect users, improve ad quality, and optimize advertiser campaigns. The work involves collaboration with teams including Google Research and DeepMind, as well as modeling teams, to build scalable advertising technology and address infrastructure and modeling challenges.

Requirements

Bachelor’s degree or equivalent practical experience.
8 years of experience programming in Python or C++.
5 years of experience leading machine learning design and optimizing end-to-end machine learning, including model deployment, model evaluation, data processing, debugging, or fine-tuning.
5 years of experience testing and launching software products.
3 years of experience with software design and architecture.
2 years of experience with GenAI techniques, such as large language models, multimodal models, or large vision models, or GenAI-related concepts such as language modeling or computer vision.

Nice To Haves

Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
8 years of experience with data structures and algorithms.
3 years of experience in a technical leadership role leading project teams and setting technical direction.
3 years of experience working in a complex, matrixed organization involving cross-functional or cross-business projects.

Responsibilities

Work across different aspects of machine learning infrastructure and modeling.
Support new modeling techniques and products by adapting training and serving infrastructure; prototype infrastructure, automation, workflows, and tooling.
Analyze and debug performance, and improve infrastructure robustness, reliability, and usability.
Collaborate with team members and partners to build systems that integrate with the broader ecosystem.
Engage with modeling teams to explore pain points and novel use cases.
Research emerging technology and make trade-off decisions between immediate improvements and longer-term stability.
Manage project priorities, deadlines, and deliverables.
Design, develop, test, deploy, maintain, and enhance software solutions.