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# Research Engineer, Machine Learning, GeminiApp Personalization, DeepMind

- **Company:** DEEPMIND MATRIX
- **Location:** Mountain View, CA, United States
- **Salary:** $174,000.0 - $252,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, C++ (Programming Language), Python (Programming Language), Machine Learning, Software Architecture, Software Engineering, Large Language Models, Information Technology, Virtual Agents, Human in the Loop, Programming Languages
- **Published:** September 10, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=e31b0b4fb3614cbd

## About the Role

* Bachelor's degree or equivalent practical experience.
 * 5 years of experience with software development in one or more programming languages.
 * 5 years of experience programming in Python or C++.
 * 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
 * 2 years of experience with Machine Learning, Large Language Models (LLMs), or applied AI.
 * 2 years of work or educational experience in machine learning and deep learning., * Master's degree or PhD in Computer Science or related technical fields.
 * 1 year of experience in a technical leadership role.
 * Experience developing accessible technologies.

## Description

We are the Gemini App team in DeepMind, building the next-generation AI assistant from Google. Be at the forefront of AI innovation with Gemini, featuring native multimodality, an expansive context window (up to 2 million tokens), and performance. Our mission is to empower billions of people by providing deeply personalised and helpful products. In this role, you will be a part of a team building a personal AI assistant that is tailored to the unique interests, passions, and curiosities of individuals., * Design, prototype, and build robust, scalable, user-facing personalization features on the full Gemini App stack.

 * Perform relevant data analysis of user feedback, logs, and evaluation tasks to identify personalization-related quality issues and opportunities.
 * Propose and implement targeted quality improvements via fine-tuning or prompting.
 * Develop evaluation techniques (both automated and with a human in the loop) to assess a hill-climb on personalization quality.
 * Contribute to the development of a data flywheel, driving continuous improvement and innovation.


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