Google is looking to solve fundamental problems in AI, including long-horizon planning and reasoning, code understanding, self-improving systems, human-agent/agent-agent interactions, and revolutionize how software is designed, developed, and maintained.
Requirements
- Experience in one or more of the following areas: machine learning, large language models, agentic AI (planning, tool use, memory), reinforcement learning (RLHF, RLVF, RLGF, offline RL), or statistical learning.
- Experience building, training, and post-training large language models (LLMs).
- Experience with program analysis, program synthesis, automated program repair, formal methods, or designing developer tools.
- Experience designing and implementing novel evaluation benchmarks for LLMs, generative code models or agentic systems.
- Experience in Python software engineering and modern ML frameworks like JAX and TensorFlow.
- One of the more scientific publication submissions for conferences, journals, or public repositories (such as NeurIPS, ICML, ICLR, etc.).
- Experience original, long-term research agenda, evidenced by first-author publications that have introduced novel ideas.
Responsibilities
- Define and pursue a long-term applied research agenda to overcome fundamental limitations of LLMs in long-horizon reasoning, planning, and tool use for complex software engineering tasks.
- Design and implement novel agentic architectures, exploring frontiers such as automated workflow optimization, multi-agent decomposition, and self-improvement.
- Lead research into synthetic data generation, verification methods/environments and rewards for building data and capability flywheels for Gemini improvement.
- Develop high-fidelity evaluation frameworks that measure agent impact to software quality and developer productivity.
- Integrate principles from adjacent domains like machine learning, natural language processing, program analysis, program repair, formal methods for addressing ambiguous tasks (such as semantic bug fixing, intent inference, task decomposition).
- 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.
- Create experiments and prototyping implementations to designing new architectures, working on real-world problems that span the breadth of computer science.
Other
- PhD degree in Computer Science, a related field, or equivalent practical experience.
- 2 years of experience leading a research agenda.
- Ability to work closely with software engineers, research software engineers and program managers to translate research and prototypes into robust, scalable, and production-ready AI systems.
- Must be willing to share and publish 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.
- Google is an equal opportunity employer and is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
