About this role
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 varied learning opportunities and career pathways for those driven to achieve exceptional results through collective effort.
Minimum qualifications:
- Master's degree or PhD in Computer Science or a related technical field.
- Experience working on AI safety or responsible AI research.
Preferred qualifications:
- Master's degree or PhD in Computer Science, Engineering, or a related field with a focus on Machine Learning.
- Experience in Python and C++ for high-performance ML library development.
- Experience with Large Language Model (LLM) demitigation, adverserial robustness, harmful manipulation detection, or AI safety evaluation and mitigation.
- Experience building evaluation frameworks, benchmarks, or automated testing pipelines for ML models.
- Prior experience working directly on AI safety, or responsible AI research.
- Prototype and deliver scalable engineering solutions across the Responsibility research portfolio.
- Architect and optimize training and inference pipelines to detect and evaluate harmful manipulation behaviors in frontier language models.
- Develop post-training strategies to mitigate manipulation risks including deceptive persuasion, sycophancy, and covert influence tactics.
- Collaborate with Research Scientists to translate safety research into robust implementations and present results to cross-functional stakeholders.
- Build and maintain evaluation infrastructure to systematically track model safety performance across releases.