Junior Research Engineer (Timeseries Techniques)

Are you a researcher or engineer interested in applying your knowledge of timeseries techniques to problems involving large scale distributed systems in the Cloud? We are looking for individuals passionate about tackling issues at the intersection of academic research and industrial application in fields such as storage, compression, analytics, forecasting, prediction, and anomaly detection.

The Cloud Reliability Lab at the Huawei Ireland Research center aims to bring top-tier reliability to Huawei Cloud by solving cross-functional problems that span Hardware, Software, Networking, Monitoring, and Operations. We have teams dedicated to each of these areas, including industry veterans, academic researchers, and Ph.D. student interns. In your role, you will collaborate with local teams in Ireland, research centers throughout Europe, and engineering teams worldwide.

Responsibilities:

• Independently execute projects and workstreams within the team. An example of such a project could be enhancing the on-disk storage efficiency of a TSDB system with trillions of timeseries.

• Probe deep technical challenges and devise solutions. One example of such a problem might be diagnosing a memory leak in the timeseries query engine.

• Stay updated with the latest research on timeseries systems and enhance your personal knowledge of algorithms, machine learning methods, and industrial tools & techniques.

• Publish significant findings in relevant conferences & journals or file patents as appropriate.

Requirements:

• Ph.D. or Master’s degree in Computer Science or a similar field

• Experience in applying specialized timeseries techniques in storage, compression, or analytics to real-world problems.

• Experience in programming Python and familiarity with the Python timeseries ecosystem

• Experience with one of the following programming languages: Rust, Go, or C++

• Strong foundational understanding of at least one of the following topics: Neural networks (LSTM, CNN, etc.), Forecasting (ARIMA, exponential smoothing, etc.), Anomaly detection (MERLIN, matrix profile, etc.), Causal Inference (Granger causality, structural modeling, Bayesian methods etc.)

• Optional: Practical experience with AWS, Azure, GCP, or other cloud systems

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