After a yearlong hiatus during the global pandemic, we're extremely excited to announce that we are reconvening the Paperspace Advanced Technologies Group!
ATG Fellows undertake critical research projects related to machine learning and deep learning and play a vital role within the larger Paperspace R&D org.
ATG Fellowships run 10-15 weeks and are paid, full-time positions. The program is designed to attract Graduate and post-Graduate students who want to pursue machine learning and Deep learning research with the support and collaboration of the Paperspace engineering org.
We depend on ATG Fellows for a wide array of subject matter expertise. We work with ATG Fellows to design meaningful research projects to explore cutting edge machine learning techniques and libraries and to push the bounds of what's possible in machine learning.
This year we are looking to support three research tracks:
- Deep Tech - This research track is designed to push the limit of current machine learning algorithms, applications, or foundational knowledge. Past fellows have taken on GPU kernel development, adversarial auto-encoders, auto-ml parameter space exploration strategies, and other advanced topics in the deep learning field.
- Tooling / Interfaces - This research track is designed to push the boundaries of what is possible with ML interfaces (GUI, CLI, and others). Topics such as experiment tracking, visualization techniques, new distributed training architectures, and novel ways of modeling complex data and interactions fall under the scope of this research area. Past fellows have worked on making pre-trained deep learning models more accessible to a broader audience through new abstractions and interfaces. Model optimization for various inference architectures and device constraints is also an area of interest.
- Education / Accessibility - This research track is designed to expand the accessibility of existing ML and deep learning techniques through education and advocacy. As more and more advanced topics come from academia, there exists a growing divide between experts and novices. Furthermore, what are the best ways to open up deep learning to new audiences and users, not just domain experts? Questions of fairness, bias, explanation, openness are at the center of this conversation.
Paperspace last supported ATG Fellowships in 2018 and 2019. ATG Fellows have come from a wide array of academic backgrounds. We have hosted researchers from leading ML and AI institutions including NYU, Georgia Tech, and Harvard. Fellows from the first two cohorts have gone on to pursue PhDs, to become research scientists for leading companies, and to found venture-backed machine learning companies, like Runway.
One of the defining features of the Paperspace ATG program is the freedom to pursue research topics with the full support of Paperspace's world-class engineering org.
We have an unusually broad surface area for a software company and as a result we have subject matter expertise in everything from low-latency streaming to distributed system architecture, accelerated computing, and production-grade machine learning systems orchestration.
As an ATG Fellow, it's highly likely your work will be made available to nearly half a million Paperspace users. You'll be able to influence the future of Paperspace products while also pursuing meaningful collaborative research.
We invite you to apply today!
