Engineering Director, Impact Accelerator, DeepMind
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Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 15 years of professional experience in engineering.
- Experience with concurrent and distributed software algorithms and architectures.
- Experience with cross-functional technical innovation.
- Experience with Python and C++.
Preferred qualifications:
- Experience using Google Cloud Platform and other cloud solutions.
- Understanding the nature of AI development and deployment and what technology can and cannot do, beyond GenAI.
- Familiarity with modern hardware accelerators (GPU/TPY).
About the job
DeepMind's Impact Accelerator is a team focused on catalysing AI breakthroughs through socially beneficial projects and partnerships, including building upon our work on AlphaFold.
The DeepMind Institute (GDI) has a unique role to develop solutions and resources built on DeepMind's technologies and expertise that extend the benefits to humanity. We are a path to real world impact, beyond Google products and services or making our research public.
In this role, you will work in a close-knit team of engineers to bring the benefits of DeepMind’s technologies to the EMEA region. Responsibilities range from keeping open access tools by developing new features and libraries within production environments, to working in partnership with and advising external partner organizations.
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.
Responsibilities
- Lead and manage a team of software engineers, working with the Impact Lead and GDI Engineering Lead to prioritize impact opportunities.
- Develop, maintain and extend AI deployment solutions in response to user feedback and strategic priorities, for example web services, open source software, and data access solutions.
- Guide and advise external impact partners.
- Ensure a healthy team environment and develop the engineering talent on the team.
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