Research Engineer, Gemini Multi-turn

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.

Modern LLMs are required to support long interactions with users, such as chat and informational dialogs, code development, image editing and video sessions. In this role, you will research new approaches for analyzing, evaluating and training LLMs to enhance their mult-turn capabilities. The work would result in impact on products including Gemini, Search and Cloud, as well as in spearheading research in the field.

You will devise innovative solutions for complex modeling and evaluation issues in a changing environment where adaptability is key to meeting tight deadlines for critical launches. You will collaborate closely with research and engineering teams across Gemini, using your findings to inform the development roadmap and presenting your work regularly to both the team and executive stakeholders.

Google Research is building the next generation of intelligent systems for all Google products. To achieve this, we’re working on projects that utilize the latest computer science techniques developed by skilled software developers and research scientists. Google Research teams collaborate closely with other teams across Google, maintaining the flexibility and versatility required to adapt new projects and foci that meet the demands of the world's fast-paced business needs.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:

• Scope and drive research efforts to improve complex frontier Gemini capabilities, such as multi-turn, factuality and tool-use.
• Review the latest literature to guide research and experimental directions.
• Curate and generate data to evaluate and improve Gemini capabilities. Design and implement both human and automated evaluation strategies.
• Design and conduct supervised fine-tuning and reinforcement learning experiments to improve the performance of Gemini capabilities such as multi-turn and factuality.
• Collaborate with partners and product functions to deliver new model capabilities to production.

Minimum qualifications:

• Bachelor's degree in Computer Science, Artificial Intelligence, a related technical field, or equivalent practical experience.
• 5 years of software engineering experience using Python.
• 1 year of experience working on large language model post-training (e.g., SFT, RLHF, DPO, PPO), model alignment, or core generative model development in an industry AI lab, research institute, or frontier AI organization.
• Experience working with deep learning frameworks (e.g., JAX/Flax) for distributed systems and accelerator clusters.
• One or more first-author papers accepted at or published in an LLM-related conference such as NeurIPS, ICML, ICLR, ACL, or EMNLP.

Preferred qualifications:

• PhD degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.
• 5 years of experience in industry AI research labs or premier research institutes developing, post-training, and deploying frontier large models.
• Experience designing and scaling automated evaluation frameworks and data curation pipelines for generative models.
• Experience in complex multi-turn conversational modeling, long-context reasoning, tool-use/function calling, or search grounding.
• Experience in optimizing distributed training performance, memory efficiency, and throughput on massive accelerator topologies (e.g., model/pipeline/tensor parallelism, FSDP).
• Strong publication record with multiple first-author or core-contributor papers in premier machine learning or NLP conferences.