Staff Software Engineer, Machine Learning, Google Chat

Google LLC•Sunnyvale, CA

38d•$197,000 - $291,000

About The Position

Google Cloud's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google Cloud's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. You will anticipate our customer needs and be empowered to act like an owner, take action and innovate. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward. Google Chat is an intelligent communication app and messaging platform. As a key component of Google Workspace, it is revolutionizing the way people communicate and collaborate with each other. From 2026, it is evolving from a messaging platform into an intelligent, agentic collaboration partner for the enterprise. Our mission is to establish AI as the fundamental core of enterprise collaboration, unlocking strategic value across functions for billions of users. You will join the Chat Backend team, the powerhouse behind Chat's critical AI infrastructure. We are responsible for architecting Chat on Agency and building foundational, next-gen GenAI capabilities(Universal Knowledge Graph, Embeddings) like Personalized Assistant, RAG-based Search, AI Overviews, and Agentic Workflows. We bridge the gap between generative AI research and production-grade distributed systems. AI will change the future of work in profound ways, and our products- Gmail, Docs, Drive, Calendar, Sheets, Vids and Meet are at the forefront. From pre-computed summaries for email threads, summaries for meetings, and videos created from a document using lifelike AI avatars, our AI opportunity is huge. Our mission is to meaningfully connect people so they can create, build, and grow together and as part of the team you can build how productivity tools should work 5-10 years into the future. You will work with model builders (Google DeepMind), work with exceptional leaders, and have the ability to impact billions of users across the world.

Requirements

Bachelor's degree in Computer Science, Artificial Intelligence, or equivalent practical experience.
8 years of experience in software development, with a focus on Machine Learning, Distributed Systems, or Backend Engineering.
5 years of experience leading technical strategy and architecting large-scale ML infrastructure (e.g., designing serving layers, model evaluation frameworks, or data processing pipelines).
Experience driving the full ML development lifecycle 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., LLMs, RAG, Agentic workflows) with a focus on latency and cost.

Nice To Haves

Master's degree or PhD in Engineering, Computer Science, or a related 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.
Experience leading high-velocity teams to deliver 0 to 1 AI products in ambiguous, changing environments.
Ability to influence technical roadmaps across organizational boundaries (e.g., partnering with Research/Core ML teams) and translating complex research into reliable product features.

Responsibilities

Define the technical goal and architecture for Chat's transformation into an Agentic AI-first platform (CPA, AIO), shifting to multi-turn, reasoning-based workflows. Architect critical migrations to the Beyond/Agency stack, designing scalable, low-latency integration with Google Workspace Agents for seamless retrieval across Gmail, Drive, and Chat.
Own the retrieval augmented generation (RAG) roadmap, driving engineering to maximize recall at 100 and reduce hallucinations via advanced retrieval techniques. Architect the Quality Loop, implementing automated, data-driven evaluation frameworks to significantly cut feature development cycles.
Own the AI stack's performance, driving optimization of inference costs and end-to-end latency to scale for billions of users. Act as the primary technical liaison with DeepMind, Core ML, and other Workspace pillars to influence shared infrastructure.

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