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

Senior Software ML Engineer, AI/ML GenAI, Gemini Enterprise corporate_fare Google place Sunnyvale, CA, USA bar_chart Mid Mid Experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area.

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 job The US base salary range for this full-time position is $174,000-$252,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about . 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.