csquare is looking for a full-time remote Data Scientist in the Central European Time -Zone to join our passionate team
Our platform covers entire Deep Learning Life-Cycles helping Data Scientists securely and collaboratively streamline their activities and best leverage our underlying supercomputing-like hardware.
As ‘Squares’ we bring our passion to the team and love building the best possible platform for our users and for ourselves.
Our mission is to democratise AI 🙂
You’re great at:
- Workflows and have carried out hundreds of experiments
- Visualisation of data
- Identifying areas/features to 0ptimise and Streamline
You have exposure to:
- HPC platforms (bringing experiments from Laptop to supercomputer)
- Full-Stack Web development
If this resonates with you or someone you know, send your resume or LinkedIn profile to: firstname.lastname@example.org
What is csquare.ai?
Csquare is a high performance and secure platform enabling AI Data Scientists to intuitively and efficiently train Deep Learning models in a collaborative environment.
Csquare helps to streamline workflows, visualise datasets, perform data wrangling, design the neural network architecture and organise and document experiments from start to finish, seamlessly parallelising jobs across multiple powerful hardware nodes.
The platform has been designed and built from the ground up in a HPC supercomputing like manner, so that Data Scientists can focus on fine tuning to enhance accuracy, while the platform takes care of routine repetitive tasks, optimising training and retraining.
Csquare is available either through distributed and highly secure shared clusters (pay as you go), or it can be deployed as dedicated (cluster of) clusters to better serve larger scale research or enterprises, with the possibility to burst out to other clusters in the Csquare network.
Clusters are highly efficient, powered by renewable energy and are cooled employing Liquid Immersion Cooling, reducing the environmental footprint. They should be deployed where the heat can be reused, either connected to local/district heating systems or industrial processes, thus reducing total energy consumption.