Software Engineer, Science and Strategic Initiatives, DeepMind

Software Engineer, Science and Strategic Initiatives, Deep Mind

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Deep Mind place London, UK ;
Mountain View, CA, USA

• Master's degree or PhD in Computer Science, Artificial Intelligence, or a related field.
• Experience with LLM agents, autonomous multi-step reasoning systems, meta-learning, or self-improving ML pipelines.
• Experience with large-scale data pipelines (e.g., Apache Beam) or foundation model training and fine-tuning at scale.
• Experience working in research environments or track record of published research in relevant AI/ML conferences.
• Domain experience in life sciences, drug discovery, cybersecurity, or developer tools/coding agents.

Google Deep Mind's Science and Strategic Initiatives unit is building a new team focused on the commercialization and real-world academic impact of AI models across Science, Cybersecurity (Code Mender), and Coding Agents. We sit at the intersection of Google Deep Mind's frontier research and Google Cloud's enterprise reach, transferring breakthroughs into products that generate groundbreaking discoveries and commercial impact with exceptional institutions (Harvard, Broad Institute, Roche, AstraZeneca), leading enterprises, and internal Google teams.

We are building a self-improving meta-agent framework to automatically diagnose failure modes, generalize learnings across customer engagements, and continuously improve agent quality  look for engineers comfortable building production systems and reasoning about research problems, who thrive in ambiguity, engage directly with customers, and want to see AI agents work in the real world—not just on benchmarks.

In this role, you will design, build, and operate the scaffolding and meta-agent framework across four key failure categories. You will build diagnostic agents and automated validation pipelines to detect and remediate real-world integration issues before they impact agent quality. You will design robust evaluation frameworks to measure production performance when lab benchmarks fail, and identify when evaluation methodology itself is the root cause of perceived failures.

You will develop memory and knowledge architectures that extract, distill, and generalize insights across multi-agent deployments to prevent learnings from remaining episodic. Additionally, you will characterize model capability gaps with empirical evidence, partnering with Google Deep Mind research teams to drive targeted model improvements.

Artificial intelligence will be one of humanity's most transformative inventions. At Google Deep Mind, 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.

Google Deep Mind's Science and Strategic Initiatives unit is building a new team focused on the commercialization and real-world academic impact of AI models across Science, Cybersecurity (Code Mender), and Coding Agents. We sit at the intersection of Google Deep Mind's frontier research and Google Cloud's enterprise reach, transferring breakthroughs into products that generate groundbreaking discoveries and commercial impact with exceptional institutions (Harvard, Broad Institute, Roche, AstraZeneca), leading enterprises, and internal Google teams.

We are building a self-improving meta-agent framework to automatically diagnose failure modes, generalize learnings across customer engagements, and continuously improve agent quality  look for engineers comfortable building production systems and reasoning about…