Senior Machine Learning Engineer, GenAI, Google Cloud

Google's software engineers develop next-generation technologies that change how billions of users connect, explore, and interact with information and one another. The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services and Google Cloud. Their global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.

• Bachelor's degree or equivalent practical experience.
• 5 years of experience in ML engineering including production environments.
• 5 years of experience in software development, including experience in software design, architecture, and shipping production-grade systems.
• 3 years of experience leading technical project strategy and ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• Experience in testing and launching scalable software products.

Nice to have:

• Master's degree in Computer Science or other technical field.
• 3 years of experience in a technical leadership role leading project teams and setting technical direction.
• 3 years of experience working in a complex, matrixed organization involving cross-functional or cross-business projects.
• Experience with genAI techniques (e.g., LLMs, multimodal, large vision models) or with genAI-related concepts (e.g., evaluations, language modeling, computer vision).

• Design and build machine learning (ML) pipelines. Evaluate, integrate, and optimize ML models and agentic workflows.
• Lead the design of generative AI solutions, optimize ML infrastructure, and guide the development of data preparation and model optimization strategies.
• Design, develop, test, deploy, maintain, and enhance large-scale AI-powered platforms and applications.
• Provide technical leadership on high-impact projects. Manage project priorities, deadlines, and deliverables.
• Facilitate alignment and clarity across teams on goals, outcomes, and timelines. Influence and coach a distributed team of engineers.