Job description

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 life-cycle can tolerate. Breakthroughs frequently sit stranded in academic papers while production systems struggle with brittle orchestration and scaling bottlenecks. 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 operate as an "Ambiguity Buster," deconstruct complex requests from executive leadership, architect 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 . • 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. • 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. 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).

Requirements

Must-haves

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.
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.
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.
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.

Nice-to-haves

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
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).

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learningpythonjavajavascript

Platform: google
Effort: medium