Senior Staff AI/ML Engineer, Cloud AI Research

About the job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. 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’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. 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.

The frontier of AI is moving faster than the traditional enterprise deployment lifecycle can tolerate. Our organization exists to eliminate that "GenAI Engineering Gap." Operating as an AI-Native pathfinding engine at the intersection of foundational research, optimized ML infrastructure and rapid prototyping, we don't simply publish papers; we build the future of AI agents and deploy them at breakneck speed.

In this role, you will guide executive leadership, deconstruct the enterprise bottlenecks across Google Cloud, and architect the overarching blueprints for our agent state machines. You will direct the technical agenda for generalizable horizontal agents and vertical Forward Deployed Engineering (FDE) deployments, ensuring that our pathfinding velocity unlocks massive commercial and platform value while simultaneously pushing the boundaries of AI science.

US: $262000 - $364000 (USD) + 25% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Architect the technical framework for high-velocity "Strike Sprints," defining the gold standard for intent-driven AI topologies, reflection loops, and advanced agent state machines across the entire organization.
• Serve as the principal technical anchor for massive Forward Deployed Engineering (FDE) missions, aligning directly with VP/Director-level partners to translate speculative agentic prototypes into enterprise solutions.
• Set the architectural goals and lead the development of generalizable horizontal agents (e.g., AutoResearch and AutoFounder) that fundamentally alter engineering velocity across Google.
• Direct the research agenda to develop agent architectures, novel planning frameworks, and self-improving foundation models.
• Act as a thought leader across Google, defining centrally governed agentic playbooks, and actively publishing definitive breakthroughs at ML conferences.

Qualifications

Minimum qualifications:

• Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, or equivalent practical experience.
• 8 years of experience in software development, including experience with Python, C++, Java, or JavaScript.
• 8 years of experience leading large-scale ML system design and optimizing ML infrastructure (e.g., distributed training, model deployment, evaluation).
• Experience developing autonomous agents, multi-agent workflows, or agentic frameworks (e.g., AutoGen, CrewAI, LangGraph, ADK, tool-use/function-calling, or multi-agent).
• Experience designing, pre-training, fine-tuning, or evaluating Large Language Models (LLMs) and multimodal foundation models.

Preferred qualifications:

• PhD degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical discipline.
• Extensive track record of first-author scientific publication submissions on agents in machine learning and NLP at venues (e.g., NeurIPS, ICML, ICLR, ACL).
• Demonstrated success operating as a Senior Staff/Principal technical lead orchestrating rapid SWAT, Pathfinding, or Forward Deployed Engineering (FDE) environments at an enterprise level.
• Industry-leading expertise in RL, reward modeling, and human/AI preference alignment techniques.
• Deep mastery in developing test-time scaling methods, search-guided decoding, adversarial robustness, and complex alignment guardrails.