Staff Software Engineer, AI/ML, Generative AI, Ads Safety

Google•Mountain View, CA

2h•$207,000 - $300,000

About The Position

Google software engineers develop technologies used at massive scale across Google products. In this role, you will manage project priorities, deadlines, and deliverables, and design, develop, test, deploy, maintain, and enhance software solutions. The position focuses on Google Ads safety, where AI and Generative AI models help protect users from harm, improve ad quality, and optimize campaigns. You will work on state-of-the-art AI at scale and collaborate with teams including Google Research and DeepMind.

Requirements

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, such as model deployment, model evaluation, data processing, and debugging.
2 years of experience with Generative AI techniques, such as LLMs, multimodal models, or Large Vision Models, or with related concepts such as language modeling or computer vision.
Experience designing and operating distributed computing systems for large-scale AI/ML workloads.
Experience with Large Language Models (LLMs), Natural Language Processing, or Generative AI.

Nice To Haves

Master’s degree or PhD in Computer Science, a related field, or equivalent practical experience.
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 as a full-lifecycle ML engineer in training dataset curation, modern neural architectures, evaluation frameworks, and post-deployment iterations.
Ability to work with cross-functional teams.

Responsibilities

Design and roll out next-generation multimodal and encoder architectures across workhorse models, integrating frame-level video embeddings in partnership with Google DeepMind.
Lead the evolution of Content Safety’s teacher stack by migrating to next-generation Gemini models and establishing In-Context Learning Video.
Lead the design of Policy enforcement RAG (PeRAG), a low-latency, high-precision retrieval system backed by a unified Long-Term Memory service.
Lead the design of a greenfield Model Debug and Gap Mitigation Agent to automatically diagnose teacher-student distillation gaps.
Provide technical leadership on high-impact projects.
Manage project priorities, deadlines, and deliverables.
Design, develop, test, deploy, maintain, and enhance software solutions.