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

About the job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.

Google Ads is at the forefront of AI innovation, applying cutting-edge machine learning and Generative AI models like Gemini to power a multi-billion dollar global business.

Our work directly impacts billions of users by protecting users from harm, improving ad quality, and optimizing campaigns for advertiser return-on-investment. We foster a culture of deep collaboration, partnering closely with teams like Google Research and DeepMind to solve complex challenges. Join us to work on state-of-the-art AI, take on problems at an unparalleled scale, and build the next generation of advertising technology.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Research emerging technology and make trade-off decisions between immediate wins and long-term stability.
• Modify training and serving infrastructure to support new modeling techniques and products.
• Collaborate closely with team members and partners to build systems that integrate seamlessly across the ecosystem.
• Improve robustness, reliability, and usability of machine learning infrastructure.
• Engage with modeling teams to explore pain points and identify novel use cases.

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience in software development.
• 5 years of experience leading ML design and optimizing end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).
• 2 years of experience with state of the art GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).
• Experience building and architecting ML systems and infrastructure (e.g., for model training, evaluation, deployment, serving).
• Experience designing and operating distributed computing systems for large-scale AI/ML workloads.

Preferred qualifications:

• 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.
• Experience in Machine Learning modeling, specifically with Large Language Models (LLMs).
• Experience in distributed Machine Learning system design.

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience in software development.
• 5 years of experience leading ML design and optimizing end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).
• 2 years of experience with state of the art GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).
• Experience building and architecting ML systems and infrastructure (e.g., for model training, evaluation, deployment, serving).
• Experience designing and operating distributed computing systems for large-scale AI/ML workloads.

In most instances, this position requires in-person interviews as part of the hiring process.