The role
From Google's own posting.
The Google Display Ads (GDA) Machine Learning (ML) team is at the forefront of revolutionizing digital advertising through cutting-edge machine learning. We develop the core intelligence that powers Google's display ads, building sophisticated models, signal representations, and systems for retrieval, ranking, and pricing. The work is the secret sauce behind optimized bidding and automation, directly impacting the open and free internet.
We leverage state-of-the-art techniques, including the latest advancements in Large Language Models (LLMs) and generative retrieval, to train and deploy models at massive scale. Processing billions of training samples daily, utilizing hundreds of thousands of CPUs and TPUs, we address some of the most challenging optimization problems in the industry.
Join us to lead and collaborate with a passionate team of Machine Learning Engineers, Data Scientists, and Software Engineers, and partner with renowned research groups like Google DeepMind, Google Research, and Ads AI. Together, we create tangible value for users, publishers, advertisers, and Google, shaping the future of online advertising.
As a Staff Machine Learning Engineer and Tech Lead Manager in Google Display Ads, you will lead the team powering generative retrieval, and LLM-driven content intelligence. You will define the technical goal and architect next-generation retrieval and personalization systems operating at global scale across billions of daily impressions. In this role, you will directly manage and mentor a high-impact engineering team while partnering with Google DeepMind and Google Research to shape the future of digital advertising.
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 .
Define Technical Goal and Retrieval Architecture: Set the long-term roadmap and architect high-performance model architectures, embeddings, generative retrieval, and systems at scale.
Drive AI Innovation: Pioneer the integration of cutting-edge AI and LLMs to deliver step-change improvements in relevance, model performance, and system efficiency.
Optimize Infrastructure and Compute: Lead training and serving optimization strategies, maximizing TPU/GPU cluster utilization with high-performance production code (e.g., C++).
Manage and Mentor Engineers: Lead and grow a high-performing team of ML and software engineers, providing technical guidance, driving execution, and elevating engineering excellence.
Collaborate Across Functions: Partner cross-functionally with ML Research, Product Management, and Infrastructure teams to translate advanced concepts into impactful, production-ready systems.
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 ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
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).
2 years of experience in a people management or team leadership role.
Experience in software engineering for recommendation systems or feed ranking architectures.
Preferred qualifications:
Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
3 years of experience working in a complex, matrixed organization.
Bachelor's degree or equivalent practical experience.
8 years of experience in software development.
5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
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).
2 years of experience in a people management or team leadership role.
Experience in software engineering for recommendation systems or feed ranking architectures.