Senior Applied Researcher, Machine Learning

Over the last two decades, GPU acceleration became the standard for scientific computing, with the fastest supercomputers in the world combining CPUs and GPUs. The emergence of Generative AI has created the larger GPU datacenters ever, creating the strongest ever supercomputers. These datacenters are sophisticated and thus challenging to operate. Yet, as an analogy from autonomous vehicle industry, we strive to automate it as much as possible. Therefore, today we are building an outstanding data scientist team to tackle these challenges. The emerging field of Artificial Intelligence for IT operations (AIOps) strives to apply AI with the big data generated by IT Operations processes, particularly in cloud infrastructures, to provide practical insights with the primary goal of improving availability. There are a wide variety of problems to address, and multiple use-cases, where AI capabilities can be used to improve operational efficiency. We categorize the key AIOps tasks as - incident detection (e.g. via Anomaly Detection), failure prediction, root cause analysis and automated actions. Moreover we are also working on optimization problem to increase not only the resiliency but also the performance of the datacenter.

What you'll be doing:

Explore high-level, undefined ideas and solve real-life problems using structured and unstructured data.

Craft proof-of-concept rooted in first principles that apply modern data science techniques to operation use cases.

Collaborate in a multi-disciplinary environment with domain experts in various fields such as networking, high performance computing for AI, telemetry etc.

Develop a strategic vision for our networking together with adjacent architects and research groups.

Define the data pipelines and ML architecture for SaaS for handling hyper scale data problems.

Support software developers to migrate prototyped to end-to-end pipelines that are suitable for deployment in production environments.