Orchestration of AI Services on Telco Cloud Platforms: Dynamic Models, Energy Efficiency and Edge-Cloud Deployment

Level of qualifications required : Graduate degree or equivalent

Other valued qualifications : PhD

Fonction : Temporary scientific engineer

About the research centre or Inria department

The Inria Centre at Rennes University is one of Inria's eight centres and has more than thirty research teams. The Inria Centre is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc.

Context

Funding Context: PEPR NF-MUST

This position is funded within the PEPR 5G and Networks of the Future programme, a national priority research programme (France 2030) co-directed by CEA, CNRS and IMT with a total budget of €65M. The programme aims to position France at the forefront of 5G, 6G and future network technologies across the full value chain.

The present work is part of the NF-MUST project (End-to-End Multi-domain Service Management Architecture of the Networks of the Future), which focuses on automating the provisioning and lifecycle management of multi-domain, multi-stakeholder services over highly heterogeneous and dynamically evolving future network infrastructures. NF-MUST covers end-to-end orchestration of coordination, cooperation and interaction functions to satisfy diverse service requests across multiple sectors, with strong emphasis on resource availability, security, performance and frugality. The project runs from May 2023 to December 2027 and involves partners including CNRS, Inria, CEA-List, Télécom Paris, Télécom SudParis, EURECOM and others.

This research engineer position contributes to the NF-MUST objectives by developing orchestration mechanisms for native AI services deployed over Telco Cloud infrastructure, at the intersection of AI workload management, cloud-native networking and future network architectures.

Scientific Context

5G and pre-6G networks must host heterogeneous intelligent applications — autonomous driving, augmented reality, real-time video analytics, embedded machine learning — whose requirements in terms of latency, energy, and model quality evolve dynamically. Open Telco Cloud platforms, based on Kubernetes, provide a shared infrastructure across operators for hosting cloud-native network functions and edge workloads. A major challenge remains, however: these platforms do not natively handle the specificities of AI workloads — adaptive architectures, distributed training, energy-aware inference scheduling.

This position is part of a research project aimed at designing and validating intelligent orchestration mechanisms for dynamic AI models on Telco Cloud infrastructure, covering the device–edge–cloud continuum. The solutions developed will be designed to be compatible with open standards in the field (Kubernetes/CaaS) and potentially integrable into different Telco Cloud platforms (Sylva, Nephio, or other cloud-native stacks).

Assignment

General Mission

The research engineer will contribute, according to their profile and the project's priorities, to one or more of the following research tracks:

Track A — System Prototyping and Integration

Track B — Algorithmic Development and Experimentation

Track C — Data Collection and Experimental Validation

Main activities

Track A — System Prototyping and Integration

Setup and configuration of a Kubernetes-based Telco Cloud environment (CaaS)
Deployment and containerisation of AI services (inference models, intelligent network functions) on a heterogeneous testbed platform
Development of monitoring and profiling tools (latency, energy, accuracy) for dynamic AI workloads

Track B — Algorithmic Development and Experimentation

Implementation and evaluation of orchestration algorithms (heuristics, optimisation, adaptive policies) for AI model placement across heterogeneous nodes (GPUs, NPUs, edge servers, cloud)
Design of dynamic model configuration selection policies (early-exit, compression, mixed precision) based on network conditions and available resources
Comparative evaluation against baselines (static deployment, greedy heuristics) under varying load and network conditions

Track C — Data Collection and Experimental Validation

Construction of representative evaluation scenarios: video analytics, sensor fusion, intelligent network functions (traffic prediction, anomaly detection)
Experimental measurement campaigns on the testbed, analysis of energy–latency–quality trade-offs
Contribution to the production of open-source artefacts (code, datasets, configurations) and to the writing of scientific or technical deliverables

Skills

Education: Master's degree or PhD in computer science, networking, distributed systems, or a related field.

Required skills

Strong programming skills in Python
Knowledge of machine learning frameworks and familiarity with model training and inference pipelines
Understanding of distributed systems concepts (scheduling, resource management, containerisation)

Benefits package

Subsidized meals
Partial reimbursement of public transport costs
Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
Professional equipment available (videoconferencing, loan of computer equipment, etc.)
Social, cultural and sports events and activities
Access to vocational training
Social security coverage

Remuneration

monthly gross salary from 2675 euros according to diploma and experience

Apply for this position

General Information

Theme/Domain : Networks and Telecommunications
System & Networks (BAP E)
Town/city : Rennes
Inria Center : Centre Inria de l'Université de Rennes
Starting date : 2026-10-01
Duration of contract : 9 months
Deadline to apply : 2026-07-31

Warning : you must enter your e-mail address in order to save your application to Inria. Applications must be submitted online on the Inria website. Processing of applications sent from other channels is not guaranteed.

Instruction to apply

Please submit online : your resume, cover letter and letters of recommendation eventually

Defence Security :
This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST).Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment.

Recruitment Policy :
As part of its diversity policy, all Inria positions are accessible to people with disabilities.

Contacts

Inria Team : ERMINE
Recruiter :
Hadjadj-aoul Yassine / Yassine.Hadjadj-aoul@irisa.fr

The keys to success

More than a checklist of technical skills, what will make this assignment a success is a particular mindset and a certain way of engaging with research and engineering work.

The ideal candidate is someone who genuinely enjoys operating at the boundary between systems and ideas — someone who finds satisfaction not only in making things work, but in understanding why they work and what they reveal about the underlying problem. This role sits at the crossroads of distributed systems, AI, and networking: an intellectual appetite for all three, even without deep expertise in each, will go a long way.

We are looking for someone with:

A taste for experimentation and hands-on work. You enjoy building things, running experiments, and letting measurements guide your thinking. You are not deterred by a system that does not behave as expected — you are curious about why.
Comfort with open-ended problems. The scope of this project will evolve. The right candidate embraces this flexibility rather than seeking rigid task definitions, and is able to self-direct their work within a broader research agenda.
A collaborative and communicative nature. The project involves a multi-partner national programme (PEPR NF-MUST). You will interact with researchers from different institutions and backgrounds, and you are able to share your progress, your doubts and your findings clearly and constructively.
Cross-disciplinary curiosity. Whether your background is closer to systems, algorithms, or networking, what matters is a genuine interest in the neighbouring fields and a willingness to build bridges between them.
A research-oriented mindset. You are comfortable reading technical literature, situating your work in a broader scientific context, and contributing to written outputs that go beyond code documentation.

A thesis or significant project in the areas of network function virtualisation, edge computing, machine learning systems, or distributed optimisation would be a genuine asset. What matters most is the drive to produce rigorous, reproducible, and impactful work within a stimulating and supportive research environment.

About Inria

Inria is the French national research institute dedicated to digital science and technology. It employs 2,600 people. Its 200 agile project teams, generally run jointly with academic partners, include more than 3,500 scientists and engineers working to meet the challenges of digital technology, often at the interface with other disciplines. The Institute also employs numerous talents in over forty different professions. 900 research support staff contribute to the preparation and development of scientific and entrepreneurial projects that have a worldwide impact.