Research Scientist, Cloud AI Research

• 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).