Senior AI/ML Research Engineer, Cloud AI Research and AI Rapid Response

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. As a Senior AI/ML Research Engineer, you are a technical driver and applied researcher. You will trade long-term operational maintenance for rapid pathfinding velocity, independently leading the deployment of foundational AI breakthroughs into massive enterprise scale. Operating as an "Ambiguity Buster," you will deconstruct complex strategic requests, design robust agent state machines, and prove viability through functional Thinnest Viable Proofs (TVPs). You will independently lead engineering workstreams to build both generalizable horizontal agents and vertical enterprise solutions, taking novel agentic prototypes directly into the field to solve real customer bottlenecks while mentoring junior engineers. The Google Cloud AI Research team addresses AI challenges motivated by Google Cloud’s mission of bringing AI to tech, healthcare, finance, retail and many other industries. We work on a range of unique problems focused on research topics that maximize scientific and real-world impact, aiming to push the state-of-the-art in AI and share findings with the broader research community. We also collaborate with product teams to bring innovations to real-world impact that benefits our customers. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google . Lead 2–4 week "Strike Sprints" to rapidly prototype advanced agent state machines, tool-use orchestration, and reflection loops, transforming boilerplate programming into intent-driven AI topologies. Partner with product and engineering teams via Forward Deployed Engineering (FDE), embedding into their workflows to translate novel agentic prototypes into scalable enterprise solutions. Architect and build generalizable horizontal agents (e.g., AutoResearch and AutoFounder) to accelerate engineering/researcher velocity. Develop agent architectures, planning frameworks, and self-improving foundation models to advance autonomous AI systems. Collaborate across Google to disseminate technical playbooks and optionally publish breakthroughs at ML conferences. Minimum qualifications: Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, a related technical field, or equivalent practical experience. 5 years of experience in software development, including coding experience in Python, C++, Java, JavaScript, or R. 3 years of experience in ML system design and optimizing ML infrastructure (e.g., distributed training, model deployment, evaluation, data processing). Experience developing autonomous agents, multi-agent workflows, or agentic frameworks (e.g., AutoGen, CrewAI, LangGraph, ADK, tool-use/function-calling, or multi-agent RL). Experience designing, pre-training, fine-tuning, or evaluating Large Language Models (LLMs) and multimodal foundation models. Preferred qualifications: Master's degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related technical discipline. First-author scientific publication submission(s) on agents in machine learning and NLP for conferences, journals, or public repositories (e.g., NeurIPS, ICML, ICLR, ACL). Experience independently driving engineering delivery in rapid SWAT, Pathfinding, or Forward Deployed Engineering (FDE) environments. Experience building complex evaluation benchmarks, test harnesses, or simulation environments. Experience in RL, reward modeling, and human/AI preference alignment techniques (e.g., RLHF, RLAIF, DPO).