The role
From Google's own posting.
At Google, research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.
From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.
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: $147000 - $210000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google .
Work on long term projects to enable new research, which involve multiple team members and require good software engineering and design.
Respond to time-sensitive issues arising in large-scale training runs, and in ongoing research.
Make systematic improvements to technical workflows and tooling used across pretraining.
Apply engineering expertise to help researchers navigate technical challenges. This requires both a deep understanding of the codebase and a broad understanding of the tech stack, the model architecture and data pipeline.
Develop the right balance between good engineering practices and having a flexible codebase which researchers find easy to use and extend.
Minimum qualifications:
Bachelor's degree in Computer Science, or a related technical field, or equivalent practical experience.
2 years of experience with software engineering involving Python-based ML/scientific libraries such as JAX or internship experience in these areas or Open Source Software (OSS) contributions.
1 year of experience with machine learning.
Preferred qualifications:
Experience with large-scale system design, distributed systems, or performance engineering.
A good understanding of engineering best practices, codebase design, and testing.
Familiarity with how modern large language models are trained.
Ability to collaborate with, and communicate effectively with, people with different skill sets and needs, and who may be distributed across different time zones.
Bachelor's degree in Computer Science, or a related technical field, or equivalent practical experience.
2 years of experience with software engineering involving Python-based ML/scientific libraries such as JAX or internship experience in these areas or Open Source Software (OSS) contributions.
1 year of experience with machine learning.