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Technical Lead Manager, Machine Learning, GeminiApp Personalization, DeepMind
GoogleSydney NSW, AustraliaSenior8+ years
Apply Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
- 5 years of experience with design and architecture; and testing/launching software products.
- 3 years of experience with Generative AI, Large Language Models (LLMs), Machine Learning, and related frameworks.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures and algorithms.
- 5 years of experience in a technical leadership role leading project teams and setting technical direction.
- 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
About the job
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.
As a Tech Lead Manager (TLM) for the Gemini App Personalization team, you will lead a team of software engineers while architecting and driving systems that make Gemini a deeply personal AI assistant—one that understands users, retains their context, and adapts to their needs across every surface. You will guide both people and technical strategy across the end-to-end personalization serving stack, ranging from high-performance distributed systems that retrieve and organize personal context to the LLM context assembly, prompt injection, and model serving configurations that shape how Gemini reasons over personal data.
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.
US: $262000 - $365000 (USD) + 25% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Manage and grow a high-performing engineering team, fostering a culture of technical excellence, innovation, psychological safety, and operational excellence. Hire, mentor, and develop engineers at all levels.
- Architect and own the personalization serving stack, from distributed user profile retrieval systems through real-time context assembly and dynamic context injection into LLM model prompts, ensuring scale across hundreds of millions of users.
- Lead the design of LLM integration points for personalization, including system instruction authoring and optimization, model serving configuration, and experiment rollout across multiple model tiers and modalities.
- Drive innovation in core personalization and long-term memory, advancing how assistant captures, synthesizes, retains, and surfaces relevant personal context across user interactions.
- Partner with research scientists, product managers, and cross-functional teams (privacy, safety, security, and core platform infrastructure) to advance personalization while protecting user privacy and data security.
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