Remote Time Series Systems Research Engineer (Streaming)

Inria; Paris; Hybride; EUR 42 000 - 56 000; Plein temps

Avantages offerts par ce poste

Teleworking available

Leave: 7 weeks + RTT + exceptional-lea

Transport reimbursement

Equipment provided

Social and cultural events

Résumé du poste

Inria, hosted at ENS Paris, is seeking a Research Engineer to support the development of prototypes for multivariate and streaming time series analysis within the Cybertéi-Demo project, in partnership with Scality.

You will collaborate with a Postdoctoral Researcher and the project team to implement novel algorithms, manage data pipelines, and validate research outcomes, contributing to scalable, reproducible systems and industry-aligned solutions.

Qualifications

• Master's degree (or equivalent) in Computer Science, Data Engineering, Data Science, or related field.
• Solid programming skills in Python, C++, or Java.
• Familiarity with time series management and analytics systems.
• Experience in systems development, benchmarking, or data engineering is a strong plus.

Responsabilités

• Design and implement efficient pipelines for ingesting and processing multivariate time series data.
• Build and maintain prototypes and experimental tools for time series indexing, querying, and anomaly detection.
• Collaborate on the integration of research outputs into scalable systems.
• Support reproducible research: code quality, documentation, and experiment tracking.
• Interface with industry partner Scality to align development with practical needs.

Connaissances

Python

C++

Java

Time series analytics

Systems development

Formation

Master's degree in Computer Science / Data Science / related field

Outils

Time series management tools

Description du poste

Inria, hosted at ENS Paris, is seeking a Research Engineer to support the development of prototypes for multivariate and streaming time series analysis within the Cybertéi-Demo project, in partnership with Scality.

You will collaborate with a Postdoctoral Researcher and the project team to implement novel algorithms, manage data pipelines, and validate research outcomes, contributing to scalable, reproducible systems and industry-aligned solutions.