Research Engineer, Frontier AI, Incubation, DeepMind

• Design, train, and optimize foundational algorithms and machine learning systems (e.g., personalized model adaptation, agentic workflows, contextual memory architectures, dynamic prompt optimization, and multimodal reasoning).
• Lead end-to-end technical development from algorithmic design and experimental prototyping to production-grade architecture and scaled serving infrastructure.
• Partner directly with engineering and product teams to integrate and harden core technologies within production environments (e.g., Project Helix, agent workspaces, and intelligent system integrations).
• Formulate novel automated and human-in-the-loop evaluation methodologies to measure capability gains, latency/compute efficiency, alignment, and personalization fidelity.

Minimum qualifications:

• Bachelor's degree in Computer Science, Machine Learning, Mathematics, Statistics, a related technical field, or equivalent practical experience.
  
• Experience programming in Python or C++.
• Experience with machine learning, algorithm design, data structures, and distributed software systems.
• Experience taking technical projects or machine learning systems from conceptual formulation to implementation and deployment.

Preferred qualifications:

• Experience developing, fine-tuning, or optimizing foundation models including techniques such as RLHF/RLAIF, supervised fine-tuning, parameter-efficient tuning, or inference optimization.
• Experience with personalization, adaptive systems, user modeling, retrieval-augmented generation, or agentic memory architectures.
• Experience with modern machine learning frameworks and model training or serving infrastructure.
• Experience collaborating across research and product boundaries to co-design technical architectures.

• Bachelor's degree in Computer Science, Machine Learning, Mathematics, Statistics, a related technical field, or equivalent practical experience.
  
• Experience programming in Python or C++.
• Experience with machine learning, algorithm design, data structures, and distributed software systems.
• Experience taking technical projects or machine learning systems from conceptual formulation to implementation and deployment.