Job description
Google 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 Google DeepMind Institute (GDI) has a unique role in GDM, to develop solutions and resources built on GDM'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 GDM’s technologies to the APAC region.
Responsibilities range from keeping open access tools best in class by developing new features and libraries within production environments, to working in partnership with and advising external partner organisations.
Job responsibilities
- Lead and manage a team of Software and Research Engineers, working with the Impact Lead and GDI Engineering Lead to prioritise 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.
- Apply your software engineering expertise to guide and advise external impact partners.
- Ensure a healthy team environment and develop the engineering talent on the team.
The role will suit candidates who enjoy applying state-of-the-art AI to important scientific and real-world problems for maximum impact for the wider community.
Minimum qualifications
- A bachelor degree in computer science, electrical engineering or equivalent experience.
- 4 years of experience in a technical leadership role; overseeing projects, with 2 years of experience in a people management, supervision/team leadership role.
- 8 years of experience in software development with C++ and Python.
- Experience with concurrent and distributed software algorithms and architectures.
- Experience with deployment in production environments.
Preferred qualifications
- Experience with cloud computing platforms and infrastructure (e.g., Google Cloud Platform, AWS, Azure).
- Experience with modern hardware accelerators (e.g., GPU/TPU).
