Research Engineer, Machine Learning, GeminiApp Personalization, DeepMind
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Minimum qualifications:
- 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.
Preferred qualifications:
- 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.
About the job
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.
Artificial intelligence will be one of humanity’s most transformative inventions. At DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.
US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- 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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