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# Research Engineer, Frontier AI, Incubation, DeepMind

- **Company:** Google LLC
- **Location:** Mountain View, CA, United States
- **Salary:** $174,000.0 - $252,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Algorithm Design, Data Structures, Tensorflow, Software Systems, Retrieval-Augmented Generation, Machine Learning Operations
- **Published:** September 2, 2026
- **Apply:** https://dejobs.org/x/x/FEE58CD386F44491836B1334DB48EC1F/job/

## About the Role

* 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., * 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.

## Description

* 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.


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