Research Engineer, Gemini Multi-turn

• 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.

• 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.