Senior AI/ML Research Engineer, Cloud AI Research

Minimum qualifications:

• Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, 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 Ph.D. 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).
• Proven 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).

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 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.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

Responsibilities:

• 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 paublish breakthroughs at ML conferences.