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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Research Engineer, Gemini Information 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, Bioinformatics, C++ (Programming Language), Information Retrieval, Python (Programming Language), Machine Learning, Recommender Systems, Software Tools, Software Engineering, Reinforcement Learning, Pytorch, Large Language Models, Generative AI, Information Technology, Google Gemini, DeepMind, Programming Languages - **Published:** October 14, 2026 - **Apply:** https://www.techcareers.com/job.asp?id=3445793391&tx=JP5751FFL&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54 ## About the Role * PHD in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related technical field, or equivalent practical experience. * 5 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 one or more programming languages or frameworks (e.g., Python, C++, JAX, PyTorch), 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 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 opportunities in the intersection of Gemini, Search, factuality, continual learning, deep research, and more. * Analyze models and model-driven products beyond what the current leaderboards show and understand the problem space and create new ways to measure ideal behavior. * Use your findings to create practical interventions to improve our models across all downstream surfaces. * Collaborate with product teams (e.g., Search, YouTube) to scale research into production environments. 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