Research Engineer, Gemini Code Post-training, DeepMind

• Drive post-training research and engineering using reinforcement learning (RL) and supervised fine-tuning (SFT) to advance Gemini coding capabilities across web, 3D, game, and mobile development.
• Develop and scale agentic post-training pipelines and landing recipes in collaboration with Operations Research (OR) teams to establish industry-leading benchmark performance in Code Arena.
• Design, build, and maintain frontier evaluation suites and automated benchmarks to measure, stress-test, and improve agentic coding capabilities.
• Implement training infrastructure, reward models, and data curation workflows to accelerate iterative model improvements from revision to revision.

Minimum qualifications:

• Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience.
• 5 years of experience in software engineering or research developing machine learning models with frameworks such as JAX, PyTorch, or TensorFlow.
• Experience conducting post-training, reinforcement learning (RL), or supervised fine-tuning (SFT) for large language models (LLMs).
• Experience designing or running LLM evaluation benchmarks and measurement pipelines.

Preferred qualifications:

• Experience in agentic coding workflows, code generation, or software engineering tasks across web, mobile, 3D, or game development.
• Experience with competitive coding benchmarks, Code Arena, or public model leaderboards.
• Experience scaling distributed training pipelines on TPU or GPU accelerators.
• Experience with reward modeling, preference optimization, or synthetic data generation for code models.
• Experience working in fast-paced research environments delivering iterative model releases.

• Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience.
• 5 years of experience in software engineering or research developing machine learning models with frameworks such as JAX, PyTorch, or TensorFlow.
• Experience conducting post-training, reinforcement learning (RL), or supervised fine-tuning (SFT) for large language models (LLMs).
• Experience designing or running LLM evaluation benchmarks and measurement pipelines.