Engineer Position on Fairness and Privacy in Trustworthy Machine Learning (F/M)

inria; France; On-site; EUR 34,000 - 52,000; Full time

Benefits offered by this job

Remboursement partiel des frais de dém

Congés annuels + RTT

Télétravail possible

Équipement professionnel disponible

Événements sociaux et sportifs

Job summary

Inria, le laboratoire national français dédié aux sciences du numérique, recrute un ingénieur/chercheur pour le MAGNET à Lille. Le candidat développera des méthodes d’équité et de confidentialité pour l’apprentissage automatique et contribuera à rendre FairGrad plus accessible, en s’intégrant avec des librairies existantes et des données locales privées.

Vous travaillerez sous supervision et collaborerez avec des chercheurs et ingénieurs, en veillant à des bibliothèques robustes et à des

Qualifications

• Bonne maîtrise de l’anglais.
• Maîtrise du Python.
• Forte expérience en implémentation et expérimentation.
• Solide formation en mathématiques.
• Bonne connaissance du machine learning, des statistiques et des algorithmes.
• Des connaissances sur l’équité et/ou la confidentialité seraient un plus.
• Capacité à collaborer au sein de l’équipe et avec des chercheurs/ingénieurs externes.
• Organisation du travail et équilibre des priorités.
• Rapport régulier des avancées.

Responsibilities

• Revoir et suivre les librairies existantes sur l’IA équitable et la confidentialité.
• Maintenir, tester et étendre la librairie FairGrad.
• Implémenter, maintenir et évaluer un pipeline d’apprentissage sur données localement privées.

Skills

Anglais

Python

Implémentation

Expérimentation

Mathématiques

Machine learning

Statistiques

Algorithmes

Fairness/Privacy

Collaboration

Organisation

Rapport régulier

Tools

Python

FairGrad

AIF360/Fairlearn

Scikit-learn

Job description

Inria, the French national research institute for the digital sciences

Organisation/Company Inria, the French national research institute for the digital sciences Research Field Computer science Researcher Profile First Stage Researcher (R1) Application Deadline 27 Nov 2026 - 00:00 (UTC) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 38.5 Offer Starting Date 1 Mar 2027 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number 2026-10659 Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The selected candidate will be based in Lille in the MAGNET team . The main objective of the team is to develop ethically acceptable machine learning algorithms focusing on fairness, privacy, and decentralized learning and to empower end users of artificial intelligence. The candidate will be under the supervision of Michaël Perrot.

Machine learning is nowadays used in various applications, such as medical diagnosis and speech recognition. Its success stems from the performance of learned models, sometimes reaching human-level capabilities. However, deploying these models on a larger scale requires more than just accuracy, and it is imperative to consider fairness and privacy when human lives are affected. For instance, a model used for medical diagnosis or an automated hiring process should not be biased against subgroups of the population. Similarly, released models should not leak information about the data that was used to train them. Several approaches have been proposed in the literature to enforce fairness and privacy (either individually or jointly) in machine learning models.

The goal of this engineer position will be to make fairness and privacy preserving approaches developed in the team more accessible to the machine learning community. On the one hand, the FairGrad library was developed a few years ago as an easy to use approach to enforce fairness. A first objective will thus be to expand it, benchmark it, and integrate it to existing fairness libraries (such as AIF360 and Fairlearn ) as well as general purpose machine learning libraries (such as scikit-learn ). On the other hand, recent work in the team showed that it is possible to learn from locally private data with a controlled bias [1]. A second goal will thus be to propose an efficient implementation of the proposed approach and to benchmark it on a variety of tasks. Finally, a third objective will be to integrate both methods with each other to propose a well founded pipeline to learn privacy preserving and fair machine learning models.

• Review and follow the existing libraries on Fair and Privacy preserving Machine Learning.
• Maintain, benchmark and expand the scope of the FairGrad library.
• Implement, maintain, and benchmark a machine learning pipeline for machine learning from locally private data.

A good candidate will have the following skills:

• A good command of English
• A good command of Python
• A strong background in implementation and experimentation
• A strong background in mathematics
• A good knowledge of machine learning, statistics and algorithms
• Some knowledge on fairness and/or privacy would be a plus
• Collaborate in the team and where applicable with external researchers and engineers
• Organize work efficiently and make a good balance between the several priorities
• Report regularly

Languages FRENCH Level Basic

Languages ENGLISH Level Good

Additional Information

• 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 and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities