Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 2 years of experience with software development in Python and C programming languages, or 1 year of experience with an advanced degree.
- 1 year of experience with Machine Learning, specifically reinforcement learning (e.g., sequential decision making).
- 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging).
Preferred qualifications:- Master's degree or PhD in Computer Science or related technical fields.
- 2 years of experience with data structures and algorithms.
- Experience with personalization, recommender systems, or multimodal machine learning.
- Experience developing accessible technologies.
- Strong problem-solving, collaboration, and communication skills.
About the jobAs a part of the Ember team, you will help reimagine Google Images, a platform serving over a billion daily interactions, to build a queryless, hyper-personalized feed that fuels creativity worldwide.
In Google Search, we're reimagining what it means to search for information - any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on. In joining the Search team, you'll have an opportunity to make an impact on billions of people globally.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $147000 - $210000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Design and build scalable ML infrastructure for Ember's 0-to-1 ranking and feed blending platform.
- Apply advanced ML and multi-objective optimization techniques to curate a hyper-personalized, inspiring visual feed for global users.
- Lead the technical execution of novel, GenAI-driven user experiences from ideation to production rollout.
- Drive the end-to-end machine learning life-cycle, data gathering, model training, offline/online evaluation, and deployment at a massive scale.
- Collaborate with Product Managers, UX, and cross-functional engineering teams to define technical paths to launch and deliver on the product roadmap.
