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

Engineer Position on Fairness and Privacy in Trustworthy Machine Learning (F/M) Download job offer in PDF format Contract type : Fixed-term contract Level of qualifications required : Graduate degree or equivalent Fonction : Temporary scientific engineer Level of experience : Recently graduated About the research centre or Inria department Created in 2008, the Inria center at the University of Lille employs 360 people, including 305 scientists in 16 research teams. Recognized for its strong involvement in the socio-economic development of the Hauts-De-France region, the Inria center at the University of Lille maintains a close relationship with large companies and SMEs. By fostering synergies between researchers and industry, Inria contributes to the transfer of skills and expertise in the field of digital technologies, and provides access to the best of European and international research for the benefit of innovation and businesses, particularly in the region. For over 10 years, the Inria center at the University of Lille has been at the heart of Lille's university and scientific ecosystem, as well as at the heart of Frenchtech, with a technology showroom based on avenue de Bretagne in Lille, on the EuraTechnologies site of economic excellence dedicated to information and communication technologies (ICT). Context 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. Assignment 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. [1] Learning with Locally Private Examples by Inverse Weierstrass Private Stochastic Gradient Descent, Jean Dufraiche, Paul Mangold, Michael Perrot, Marc Tommasi, 2026. Main activities • 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. Skills 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 Please follow the instructions given in https://team.inria.fr/magnet/how-to-apply/ to set up your application file. 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 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 Salary depending on profile Apply for this position Share • Facebook • Linkedin • Twitter • Email General Information • Theme/Domain : Optimization, machine learning and statistical methods Statistics (Big data) (BAP E) • Town/city : Villeneuve d'Ascq • Inria Center : Centre Inria de l'Université de Lille • Starting date : 2027-03-01 • Duration of contract : 1 year, 6 months • Deadline to apply : 2026-11-27 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 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 : MAGNET • Recruiter : Perrot Michael / michael.perrot@inria.fr The keys to success A successful candidate will • 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 About Inria Inria, the French national institute for research in digital science and technology, supports the French government in national research and innovation strategies in the digital field, acting as Digital Programs Agency. Inria leads over 300 research and innovation projects with its 3,500 scientists, engineers, and support staff, in partnership with universities and the digital ecosystem (businesses, entrepreneurs, and public stakeholders). Together, we explore strategic fields such as artificial intelligence, cybersecurity, quantum computing, cloud technologies, digital transformation in healthcare, digital twins, and digital technologies for defence. We develop practical solutions such as software, tech startups, partnerships with national companies, and cutting-edge training programmes. Our goal is to drive scientific, technological, and industrial excellence to ensure France’s digital sovereignty.