Staff Software Engineer, Machine Learning, Google Chat

Staff Software Engineer, Machine Learning, Google Chat corporate_fare Google place Sunnyvale, CA, USA bar_chart Advanced Advanced Experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders; deep expertise in domain.

Minimum qualifications:

Bachelor's degree in Computer Science, Artificial Intelligence, or equivalent practical experience. 8 years of software engineering experience. 5 years of experience leading technical strategy and architecting large-scale ML infrastructure and distributed systems. Experience managing the full ML development life-cycle, from hypothesis and data collection to fine-tuning, evaluation, and post-production monitoring. Experience building, optimizing, and deploying Generative AI systems in production (e.g., Large Language Model (LLMs), RAG, Agentic workflows) with a focus on latency and cost. Experience designing scalable database solutions using Cloud Spanner or similar globally distributed databases.

Preferred qualifications:

Master's degree or PhD in Computer Science, Machine Learning, Computer Engineering, or a related highly technical field. Experience optimizing Inference cost (TPU/GPU) and latency for real-time, user-facing applications (e.g., quantization, caching, speculative decoding). Experience in Retrieval Augmented Generation (RAG) architectures, including vector search, embedding optimization, and semantic retrieval strategies. Ability to influence technical roadmaps across organizational boundaries (e.g., partnering with Research/Core ML teams) and translating research into reliable product features. About the job Google Cloud's mission is to make every business successful through AI by combining cutting-edge technology, infrastructure, and talent. AI/ML software engineers in Cloud bridge the gap between pioneering models and a massive product vehicle reaching billions. Our talent density and AI-powered tools drive rapid development, rooted in a culture of empowerment and a bias to action. In this role, you aren't just building technology; you're shaping the frontier of enterprise and driving the evolution of advanced models. In this role, you will be a part of the Chat Back-end team, building and shaping the critical AI infrastructure that powers Google Chat, one of the fastest-growing products within Google Workspace and a platform transforming how billions of people communicate and collaborate. As a part of a team evolving into an intelligent, agentic collaboration partner for the enterprise, you will contribute to establishing AI as the foundation of enterprise collaboration by architecting Agents in Chat and developing foundational, next-gen GenAI capabilities, including Universal Knowledge Graph, Embeddings, Personalized Assistant, RAG-based Search, AI Overviews, and Agentic Workflows, while bridging the gap between generative AI research and production-grade distributed systems. The US base salary range for this full-time position is $207,000-$300,000 + 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 shift from reactive manual triage to an AI-scaled, automated evaluation loop, while architecting the pipeline, bridging unstructured user feedback to verifiable Continuous Integration (CI)/Continuous Delivery (CD) code mutation by exploring and driving new techniques. Own the technical roadmap for retrieval augmented generation, driving Recall@100 and eliminating hallucinations by integrating advanced new-generation embedding-based retrieval (EBS), cross-corpus signal ingestion, and vector search methodologies. Automate manual hill-climbing with data-driven frameworks, aiming a reduction in Time-to-Resolution (TTR) for Natural Language Understanding (NLU) bugs, dropping feature development life-cycles from weeks to hours. Optimize the performance and efficiency of the AI stack, leading initiatives to reduce inference costs through TPU/GPU utilization improvements and minimizing end-to-end latency for real-time features. Act as the primary technical liaison with Google DeepMind, Core Machine Learning, Product Managers and other Workspace pillars.