Research Scientist, Cloud AI Research

As an organization, Google maintains a portfolio of research projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work. As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world. 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: $147000 - $210000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google . Develop agent architectures, planning frameworks, and self-improving foundation models to advance autonomous AI systems. Design and deliver vertical agents across core domains (e.g., infrastructure, forecasting, and optimization) targeting operational efficiencies. Build generalizable horizontal agents to accelerate researcher velocity and automate end-to-end business value generation across Google. Partner with product and engineering teams to translate novel agentic prototypes into scalable enterprise solutions and products. Publish breakthroughs at ML conferences, establish benchmarks, and collaborate across Google. Minimum qualifications: PhD in Computer Science, a related field, or equivalent practical experience. Experience developing autonomous agents, multi-agent workflows, or agentic frameworks (e.g., AutoGen, CrewAI, LangGraph, tool-use/function-calling, or multi-agent RL). Experience designing, pre-training, fine-tuning, or evaluating Large Language Models (LLMs) and multimodal foundation models. Experience writing code in Python, JavaScript, R, Java, or C++. One 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). Preferred qualifications: 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). Experience developing test-time/inference-time compute scaling methods, search-guided decoding, or planning algorithms. Experience developing safe and reliable AI systems, including adversarial, hallucination mitigation, alignment guardrails, model interpretability. PhD in Computer Science, a related field, or equivalent practical experience. Experience developing autonomous agents, multi-agent workflows, or agentic frameworks (e.g., AutoGen, CrewAI, LangGraph, tool-use/function-calling, or multi-agent RL). Experience designing, pre-training, fine-tuning, or evaluating Large Language Models (LLMs) and multimodal foundation models. Experience writing code in Python, JavaScript, R, Java, or C++. One 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).