Senior Software Engineer, Data and Infrastructure, Professional Intelligence, DeepMind

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: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about
benefits at Google
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Minimum qualifications
Bachelor's degree in Computer Science, or equivalent practical experience.
Experince in Python with modern toolchains and frameworks (e.g., uv, ruff, FastAPI, Pydantic).
Experience building and operating data pipelines (e.g., one or more of Apache Beam, Dataflow, Flink, and Spark), and with trade-offs between batch and streaming designs.
Experience with data storage solutions (e.g., SQL/Postgres, NoSQL, blob storage, search indexes, vector databases) and the trade-offs between them.
Experience using coding agents to develop code and tests to specification.

Preferred qualifications
Experience with infrastructure-as-code (e.g., Terraform) and serverless or container platforms (e.g., Cloud Run, GKE).
Experience implementing access control and security in data systems (e.g., row-level security, IAM, VPC Service Controls), or working with sensitive or regulated data.
Experience with document processing at scale (e.g., OCR, PDF/Office rendering, e-discovery or load-file formats).
Experience in start-up-like environments where priorities can change quickly, and joy in learning new technologies and teaching teammates.
Exposure to production systems that rely heavily on ML models, including batch inference with large language models.

Responsibilities
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.
Engineer for production an intense focus on code and product quality, testing, CI/CD, monitoring and on-call. Keep live systems healthy while the next generation is built.
Implement fine-grained, per-document access controls across search indexes, databases and storage, and make sure every access is logged.
Multiply your impact with AI: use the latest AI coding agents to move fast, and help the team do the same.