The role
From Google's own posting.
At Google, research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.
From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.
Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.
We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.
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 .
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.