Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
- Experience in full-stack engineering and in user-facing product development, encompassing frontend (e.g., web or mobile) and backend services.
- Experience with software design and architecture.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- Proven ability to navigate quality hillclimb processes and utilize strong quantitative analytical skills to identify product opportunities.
- Keen judgment in anticipating and mitigating potential risks involving privacy, legal compliance, PR, marketing, and safety.
- Track record of optimizing system performance and reducing latency.
About the job
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.
We are pushing the boundaries across multiple domains. Our global teams offer varied learning opportunities and 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: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Learn more about
benefits at Google.
Responsibilities
- Build new features for gemini.google.com by integrating cutting-edge AI capabilities.
- Deliver advanced model enhancements to gemini.google.com users.
- Elevate user-facing product quality continuously, delivering state-of-the-art model capabilities tailored to user needs.
- Solve complex technical challenges and launch high-quality products within legal, privacy, latency, and TPU cost constraints.
