Principal Research Engineer, AI Systems, DeepMind

Principal Research Engineer, AI Systems, DeepMind

Minimum qualifications:

• Bachelor's degree in related technical field, or equivalent practical experience.
• 15 years of experience in engineering or related function.

Preferred qualifications:

• Experience with the modern LLM stack (e.g., agent architectures and harnesses, multi-agent swarms, post-training/fine-tuning/RL, or large-scale LLM evaluation).
• Experience in software engineering, systems infrastructure, or AI/ML modeling using Python and C++.
• Experience with technical leadership and driving multi-team, cross-functional projects from inception to delivery.
• Experience in information retrieval, search and ranking systems, clustering, or large-scale data processing infrastructure.

About the job

Going beyond standard long-context retrieval, we are developing the Agera Kernel—a scalable engine designed for "pre-computation of expertise." The system leverages map-reduce style processing, agentic exploration, and persistent memory states to allow models to ingest, structure, and deeply understand complex domains prior to user interactions.

By combining automated domain pre-computation with expert human calibration in Agera Studio, we aim to produce specialized Expert Models that significantly outperform foundational models on intricate, multi-turn tasks.

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.

Responsibilities

• Partner with product and research teams across Google Deepmind (GDM) and Google to invent 0-to-1 features and products powered by Agera, identifying where Agera creates real leverage and aligning teams around a shared goal.
• Develop across the full AI systems stack (e.g., agent harnesses and isolated subagent swarms, inspectable semi-structured state, vectorized tool execution, custom retrieval indices and universal reranking, large-scale evals, human-in-the-loop Studio UIs, and fine-tuning/expertise distillation into weights and skills).
• Balance rapid, exploratory prototyping with the discipline to write clean, scalable code.
• Lead complex engagements end-to-end, bridging different team roadmaps and constraints, setting technical direction, and driving convergence through working prototypes.