Research Engineer, Machine Learning, GeminiApp Personalization, DeepMind
Research engineer on Google DeepMind's Gemini App personalization team, building scalable user-facing personalization features across the full Gemini stack. Day-to-day: analyzing user feedback and logs, improving quality via fine-tuning or prompting, and building automated and human-in-the-loop evaluations. Core stack: Python or C++, TensorFlow, ML/recommender systems, LLMs.
- Compensation
- $174K–$252K / yr
- Location
- Mountain View, CA, USA; San Francisco, CA, USA
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 1+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- Bachelor's degree or equivalent practical experience
- 5 years of experience with software development in one or more programming languages
- 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture
- 3 years of experience with machine learning algorithms and tools (e.g., TensorFlow), recommender systems, deep learning, or personalization
- 3 years of experience with software development in Python or C++
Nice to have
- Master's degree or PhD in Computer Science or related technical field
- 1 year of experience in a technical leadership role
- Experience developing accessible technologies
- Experience in Natural Language Processing (NLP), Large Language Models (LLMs) and Generative AI
Full description
At Google DeepMind our mission is to build the world's first general-purpose learning agent. Central to this mission is the complex task of measuring the intelligence of our prototypes. As a Software Engineer, you will be working with the cutting edge AI agents developed by our exceptional team of Machine Learning and Neuroscience research scientists. Your responsibilities will include everything from creating systems for agent testing using 2D and 3D games to developing test problems within physics simulators. You will create graphical visualization of results, build competitive agent leaderboards and test new algorithms on robots. To succeed in this role you will need to have a strong foundation in software engineering and enjoy working on a wide range of challenging problems within a mission-driven team.
We are the Gemini App team at Google DeepMind, building Google's next-generation AI assistant. Stay at the forefront of innovation with Gemini, which features native multimodality, an expansive context window of up to 2 million tokens, and excellent performance. Our mission is to empower billions of people by providing deeply personalized and helpful products. In this role, you will help build a personal AI assistant tailored to the unique interests, passions, and curiosities of every individual.
Artificial intelligence will be one of humanity’s most transformative inventions. At Google 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.
We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
- 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 robust evaluation techniques (both automated and with a human in the loop) to assess and hill-climb on personalization quality.
- Partner closely with research scientists, engineers, and cross-functional partners (product and program managers) to advance the business goals of the organization.