The role
From Google's own posting.
At 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 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.
Artificial intelligence will be one of humanity’s most transformative inventions. At 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: $147000 - $210000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google .
Act like an owner ask questions, propose solutions, build consensus, and drive execution from concept to delivery.
Build agent capabilities design and implement skills, plugins and typed tools for LLM agents that search, read, reason over and cite large private corporate.
Make outputs verifiable build grounding and citation checks, access-aware retrieval, and automated review of agent-authored knowledge, so every result can be audited back to its sources.
Lead evaluation build evaluation suites and datasets with subject matter experts, measure factual accuracy, citation fidelity and multi-step reasoning, and use the results to drive improvements.
Ship to production strong code quality, testing, CI/CD and monitoring for agent systems used daily by professionals.
Minimum qualifications:
Bachelor's degree in Computer Science, or equivalent practical experience.
Experience in Python or TypeScript with modern toolchains and frameworks.
Experience building/shipping applications powered by LLMs: prompting, tool use or function calling, retrieval-augmented generation, and agent orchestration.
Experience designing evaluations for LLM or ML systems and using them to drive measurable quality improvements.
Experience owning production software end-to-end, from design and implementation to deployment, monitoring and iteration on user feedback.
Experience using coding agents to develop code and tests to specification.
Preferred qualifications:
Experience with agent frameworks and protocols or agentic coding environments.
Experience with search and retrieval systems and citation or grounding verification.
Experience building products for legal, finance, compliance or other regulated, high-stakes domains, or working closely with domain experts.
Exposure to fine-tuning or reinforcement learning for LLMs, and experience on Google Cloud Platform.
Experience with agent safety, red teaming, and adversarial testing of autonomous AI systems.
Experience or research background in recursive self-improvement (RSI) architectures, automated self-correction, or self-evolving agent frameworks.
Bachelor's degree in Computer Science, or equivalent practical experience.
Experience in Python or TypeScript with modern toolchains and frameworks.
Experience building/shipping applications powered by LLMs: prompting, tool use or function calling, retrieval-augmented generation, and agent orchestration.
Experience designing evaluations for LLM or ML systems and using them to drive measurable quality improvements.
Experience owning production software end-to-end, from design and implementation to deployment, monitoring and iteration on user feedback.
Experience using coding agents to develop code and tests to specification.