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# Research Engineer, Gemini Retrieval, DeepMind

- **Company:** Google LLC
- **Location:** Mountain View, CA, United States
- **Experience:** Experienced
- **Salary:** $174,000.0 - $252,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Computer Programming, Information Retrieval, Python (Programming Language), Machine Learning, Recommender Systems, Software Tools, Software Engineering, Reinforcement Learning, Pytorch, Large Language Models, Generative AI, Information Technology
- **Published:** August 9, 2026
- **Apply:** https://dejobs.org/x/x/24FD43C4373F42BC96A25FA00D1C7535/job/

## About the Role

* Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related technical field, or equivalent practical experience.
 * 3 years of experience in machine learning, with a focus on Large Language Models (LLMs) or Information Retrieval (IR).
 * 3 years of experience with software engineering in Python, C++, JAX, or PyTorch programming, including experience implementing multi-stage training pipelines or algorithms., * Experience developing Reinforcement Learning (RL) algorithms, automated evaluation systems, or LLM reflection and reasoning frameworks.
 * Experience with competitive programming, mathematics competitions (e.g., ICPC, IOI, IMO), or a track record of developing novel algorithms.
 * Experience rapidly prototyping and iterating on complex systems, generative AI models, or multi-stage pipelines.
 * Experience collaborating with product engineering teams to deploy machine learning models or research innovations into large-scale production environments (e.g., Search, Recommendation Systems).
 * Track record of learning new software tools, frameworks, and codebases quickly to solve complex technical obstacles.

## Description

* Uncover strategic opportunities at the intersection of Gemini, Search, factuality, continual learning, deep research, and domain internalization.
 * Analyze models and model-driven products beyond current leaderboards-understand complex problem spaces and create new ways to measure ideal behavior.
 * Use empirical findings to develop practical interventions and modeling innovations to improve our models across downstream surfaces.
 * Collaborate with product teams like Search and YouTube to scale research innovations into production environments, optimizing for quality and efficiency.


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