info_outline
X In most instances, this position requires in-person interviews as part of the hiring process.
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.
About the jobAs 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 .
Responsibilities - 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.
