Minimum qualifications:- Bachelor's degree in Computer Science, a related field, or equivalent practical experience.
- 5 years of experience in software development with one or more programming languages.
- 2 years of experience with core Generative Artificial Intelligence (GenAI) technologies (e.g., Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agents) or model training/post-training.
- 1 year of experience with GenAI concepts such as language modeling or computer vision.
Preferred qualifications:- Master's degree or PhD in Computer Science or a related technical field.
- Experience in machine learning modeling and tech leadership roles.
- Proficiency in Python, C .
- Experience building production-grade LLM-based applications and working with Google internal infrastructure.
- Track record of innovation and critical thinking in solving technical problems.
About the jobAs a Senior Software/Machine Learning (ML) Engineer in the Gemini Enterprise team, you will be at the heart of the generative Artificial Intelligence (AI) revolution. You won't just be testing models; you will be engineering the systems that define the intelligence, reliability, and helpfulness of Gemini Enterprise for millions of enterprise users. You will architect automated evaluation frameworks, design optimization algorithms, and solve technical issues at the intersection of Large Language Models (LLMs) and enterprise-grade software.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $252000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Lead the technical development of agentic features to transform enterprise productivity.
- 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, and resolve them by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
- Design and implement Generative Artificial Intelligence (GenAI) solutions, leverage Machine Learning (ML) infrastructure, and evaluate tradeoffs between different techniques and their application domains.
