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X Note: By applying to this position you will have an opportunity to share your preferred working location from the following:
Mountain View, CA, USA; New York, NY, USA.
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).
- 3 years of experience in a technical leadership role.
- 2 years of experience with GenAI techniques (e.g., large language models, multi-modal, large vision models) or with GenAI-related concepts (e.g., language modeling, computer vision).
- 2 years of experience in a people management or team leadership role.
Preferred qualifications: - Master's degree or PhD in Engineering, Computer Science, or a related technical field.
- 3 years of experience working in a complex, cross-functional organization.
- Experience in launching ML enabled product features to public users.
About the jobWith technical and leadership expertise, you manage engineers across multiple teams and locations, a large product budget and oversee the deployment of large-scale projects across multiple sites internationally.
Photos Ellmann team's mission is helping users thrive by unlocking the full value of a life's worth of photos.
The Ellmann Signals and Modeling team builds out and refines signals and agentic tools to support Photos AI features and Google-wide Personal Intelligence. We use multimodal large language models (LLMs), classical machine learning, and LLM-assisted workflows for data and evaluation, and do tons of prototyping. We ship with standard Google infra for large-scale data processing and serving -- ps1, server platform, and flume.
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 - Own the outcomes and technical decisions made by the team.
- Drive engineering quality and technical excellence across all team deliverables.
- Review technical designs and drive alignment for high-performing GenAI systems.
- Identify and allocate projects according to team skillset, interests, and availability.
- Collaborate with product managers to maximize quality for engineering effort and risk.
