Principal Research Engineer, AI Systems

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.

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: $307000 - $427000 (USD) + 30% bonus target + equity + benefits

Learn more about benefits at Google.

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.

Qualifications

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.