Research Engineer, Responsible Frontier AI Research, DeepMind

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 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: $207000 - $300000 (USD) + 20% bonus target + equity + benefits Learn more about benefits at Google .

Design, prototype, scale engineering solutions, and run rigorous experiments to address emerging research priorities within the Responsibility portfolio. Work with Research Scientists to advance the state of the art in Responsible AI. Act as a technical anchor for the team, establishing best practices for code quality, scalability, and system design. Present complex engineering trade-offs and research results clearly to cross-functional stakeholders and leadership.

Minimum qualifications: Bachelor's degree in Computer Science, Machine Learning, Mathematics, or a related technical field, or equivalent practical experience. 8 years of experience in machine learning engineering or large-scale software systems. 3 years of experience in Python programming 3 years of experience with ML frameworks such as JAX, PyTorch, or TensorFlow. Preferred qualifications: Master's degree or PhD in Computer Science, Engineering, or a related field with a focus on Machine Learning. Experience working directly on AI safety, or responsible AI research. Experience in Python and C++ for high-performance ML library development. Experience with harmful manipulation detection, persuasion modeling, deceptive behavior analysis, or AI safety evaluation and mitigation. Experience building evaluation frameworks, benchmarks, or automated testing pipelines for ML models.