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X In most instances, this position requires in-person interviews as part of the hiring process.
Minimum qualifications: - Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, or equivalent practical experience.
- 8 years of experience in software development, including coding experience in Python, C , Java, JavaScript, or R.
- 5 years of experience leading 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 R.
- 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.
- Experience developing test-time scaling methods, search-guided decoding, and safe AI systems (hallucination mitigation, alignment guardrails).
- Experience building evaluation benchmarks, test harnesses, or simulation environments for complex reasoning, code generation, or multimodal tasks.
- Experience in RL, reward modeling, and human/AI preference alignment techniques (e.g., RLHF, RLAIF, DPO).
- Proven experience operating as a Staff technical lead in rapid SWAT, Pathfinding, or Forward Deployed Engineering (FDE) environments writing robust production code.
- Two or more first-author scientific publication submission(s) on agents in machine learning and natural language processing for conferences, journals, or public repositories (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP).
About the jobIn this role, you will operate as an "Ambiguity Buster," deconstruct complex strategic requests from executive leadership, architect robust agent state machines and prove viability through functional Thinnest Viable Proofs (TVPs). You will write production-grade code to build both generalizable horizontal agents and vertical enterprise solutions, taking novel agentic prototypes directly into the field to solve real customer bottlenecks.
Google Cloud accelerates every organization's ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google's 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.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $207000 - $300000 (USD) 20% bonus target equity benefits
Learn more about benefits at Google .
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 targeting massive operational efficiencies.
- Build generalizable horizontal agents (e.g., AutoResearch and AutoFounder) to accelerate engineering/researcher velocity and automate end-to-end business value generation across Google.
- Develop state-of-the-art agent architectures, planning frameworks and self-improving foundation models to advance autonomous AI systems.
- Collaborate across Google to disseminate technical playbooks and agentic frameworks and optionally publish breakthroughs at ML conferences.
