Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 2 years of experience programming in Python or C .
- 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
- Experience with core GenAI concepts (LLM, Multi-Modal, Large Vision Models) and experience with text, image, video, or audio generation.
Preferred qualifications:- Master's degree or PhD in Computer Science, or a related technical field.
- Familiar with modern Large Language Model (LLM) and agent technologies, such as agent harnesses, evaluation frameworks, and hill-climbing optimization.
- Passionate about pushing the boundaries of AI adoption in the enterprise world.
About the jobThis platform will serve as the core connective tissue for all of Google Cloud, enabling seamless interaction between Google services, third-party applications, and enterprise data. We build the systems that allow an AI to reason, plan, and act. Our platform will provide the foundational backbone for first-party Google products like cloud assist and Vertex AI agents, and will empower third-party developers to build their own agentic applications.
In this role, you'll have a unique opportunity to replicate a startup experience within Google.
US: $147000 - $210000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities- Write product or system development code.
- Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
- Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
- Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
- Implement Generative Artificial Intelligence (GenAI) solutions, utilize ML infrastructure, and contribute to data preparation, optimization, and performance enhancements.
