Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 5 years of experience in software or machine learning engineering.
• 1 year of experience with one or more of the following: generative AI, large language modeling, reinforcement learning (e.g., sequential decision making), recommendation systems, or specialization in another ML field.

Preferred qualifications:

• Master's degree or PhD in Computer Science, Mathematics, Applied Statistics, Machine Learning, or equivalent practical experience in an industry setting.
• 3 years of experience coding with C++ or Python programming languages.
• Experience building or using AI Agents and productionizing large language and multimodal models.

About the job

At Google DeepMind our mission is to build the world's first general-purpose learning agent. Central to this mission is the complex task of measuring the intelligence of our prototypes. As a Software Engineer, you will be working with the cutting edge AI agents developed by our exceptional team of Machine Learning and Neuroscience research scientists. Your responsibilities will include everything from creating systems for agent testing using 2D and 3D games to developing test problems within physics simulators. You will create graphical visualization of results, build competitive agent leaderboards and test new algorithms on robots. To succeed in this role you will need to have a strong foundation in software engineering and enjoy working on a wide range of challenging problems within a mission-driven team.

Join our Deep Research team and revolutionize the way research is conducted. As a Software Engineer, you will harness the power of Gemini models to build the most advanced and reliable research agent on the market.

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.

Responsibilities

• Create and implement sophisticated agentic workflows that allow our models to intelligently use tools and data to solve complex problems.
• Engineer our runtime infrastructure for maximum speed and reliability, ensuring our SOTA models and features operate with the lowest possible latency.
• Partner closely with research scientists and product managers to translate AI research and user requirements into robust, scalable, and impactful features.
• Design and implement testing and evaluation frameworks to benchmark the performance, accuracy, and efficiency of our agentic workflows, driving continuous improvement.
• Take ownership of our production environment, leading the charge on debugging complex issues, enhancing system observability, and ensuring our agent operates with maximum uptime and reliability.