Senior Software ML Engineer, AI/ML GenAI, Gemini Enterprise

Google•Sunnyvale, CA

2d•$174,000 - $252,000

About The Position

Google Cloud is hiring a Senior Software/Machine Learning Engineer for its Gemini Enterprise team. The role focuses on engineering systems that support the intelligence, reliability, and helpfulness of Gemini Enterprise for enterprise users. The engineer will develop agentic features, automated evaluation frameworks, and optimization algorithms, and address technical challenges at the intersection of large language models and enterprise-grade software. The position also involves designing and implementing generative AI solutions, using machine learning infrastructure, and evaluating tradeoffs among techniques and their application domains. Google Cloud provides enterprise-grade solutions built on Google technology to customers in more than 200 countries and territories. Individual pay is determined by job-related skills, experience, and relevant education or training. The U.S. compensation range is $174,000–$252,000, plus a 15% bonus target, equity, and benefits.

Requirements

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 AI technologies such as large language models, retrieval-augmented generation, or agents, or with model training/post-training.
1 year of experience with generative AI concepts such as language modeling or computer vision.

Nice To Haves

Master’s degree or PhD in Computer Science or a related technical field.
Experience in machine learning modeling and technical leadership roles.
Proficiency in Python and 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.

Responsibilities

Lead the technical development of agentic features to transform enterprise productivity.
Collaborate with peers and stakeholders through design and code reviews to promote best practices, including style guidelines, code quality, accuracy, testability, and efficiency.
Contribute to documentation or educational content and adapt it based on product or program updates and user feedback.
Triage, debug, track, and resolve product or system issues by analyzing causes and effects on hardware, network, or service operations and quality.
Design and implement generative AI solutions, leverage machine learning infrastructure, and evaluate tradeoffs among techniques and their application domains.