Senior Software Engineer, Data and Infrastructure, Professional Intelligence, DeepMind
Summary
Senior software engineer at Google DeepMind building data and infrastructure for the Professional Intelligence team: data pipelines, storage and access controls, and tooling/plugins for LLM agents that evaluate AI systems. Stack centers on Python (FastAPI, Pydantic) plus Beam/Dataflow/Flink/Spark, cloud platforms, and ML-powered production systems.
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.
US: $174000 - $252000 (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.
- 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.
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.
