Staff Software Engineer, DeepMind

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.

The GenAI Eval Platform team’s mission is to provide a rigorous, high-velocity, and scalable evaluation backbone that powers the development and deployment of Google’s frontier AI models.

Working in close collaboration with Google DeepMind (GDM) research teams, you will build the evaluation platform that serves as the steering wheel for AI progress—determining model capability, safety, reasoning depth, and readiness for global launch.

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: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google . Responsibilities

• Design and optimize distributed evaluation execution engines to orchestrate large volumes of inference steps across TPU and GCU pools with high throughput and low latency.

• Build foundational abstractions for evaluating complex LLM agent loops, tool use, and automated LLM-as-a-judge rating systems.

• Design robust error classification, automated retry policies, and observability dashboards to maintain strict SLOs for evaluation pipeline success rates.

• Partner closely with GDM research scientists and data science teams to anticipate frontier model evaluation requirements and translate them into scalable infrastructure solutions.

• Mentor fellow engineers, set high standards for code quality (Python in Google3), and advocate rigorous testing and system design practices.

Minimum qualifications:

• Bachelor's degree in Computer Science, Software Engineering, Mathematics, or a related STEM field, or equivalent practical experience.

• 5 years of experience coding in one or more general purpose languages (e.g., Python, Java, C or C++).

• 5 years of experience designing and launching large-scale production software products or foundation AI systems.

• 4 years of experience in software development.

• 3 years of experience leading software design and system architecture for high-throughput distributed systems, foundation model runtimes, or client applications.

Preferred qualifications:

• Master's degree or PhD in Computer Science, Artificial Intelligence, Robotics, or a related technical field.

• 5 years of experience in a technical leadership role setting technical direction and guiding project teams.

• 3 years of experience driving complex cross-functional initiatives across research, engineering, product management, and UX design in complex matrixed environments.

• Experience developing, evaluating, or serving large language models, agentic workflows, multimodal pipelines, or AI runtime APIs (e.g., PyTorch, JAX, Triton, vLLM, Gemini API).