Engineering position on differents aspects of eXplainable AI

Le descriptif de l’offre ci-dessous est en Anglais

Type de contrat : CDD

Niveau de diplôme exigé : Bac + 5 ou équivalent

Fonction : Ingénieur scientifique contractuel

A propos du centre ou de la direction fonctionnelle

The Inria Rennes - Bretagne Atlantique Centre is one of Inria's eight centres and has more than thirty research teams. The Inria Center 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.

Contexte et atouts du poste

The recruittee will work conduct different engineering tasks, related to two projects.

1) The FAbLe project (Framework for Automatic Interpretability in Machine Learning). This project started in 2020 and is financed by an ANR(a) JCJC grant ported by Luis Galárraga, researcher at the LACODAM team in Inria - Rennes.

2) The PEPR IA, AdaptING, on the subject "Continual Learning from Knowledge Graphs". The project is leaded par Alberto Bosio from École Central de Lyon. Élisa Fromont and Luis Galárraga, from LACODAM, are affiliated to the project.

(a) French National Research Agency


Mission confiée

Under the guidance of Luis Galárraga, the recruittee's main task will be to implement and optimize some research support tools.

  1. Implementation of a (socket-based) server interface for an in-memory database tailored for rule mining on knowledge graphs. This tool will be crucial for our research on continual learning from knowledge graphs. We count on two embedded implementations of the database, one in Java, one in Rust. Depending on the candidate's competences, we will focus on one or the other.
  2. Profile the current implementation of the HIPAR algorithm for interpretable regression in order to understand how to optimize it. Depending on our findings, the candidate will be in charge of optimizing the code (potentially using Cython) and release a new version of the algorithm. This task is in indirect connection to the FAbLe project.

Principales activités

Main activities (5 maximum) :

  • Conduct regular meetings with the team members to understand the codebase
  • Design, implement, and test the server interface for the in-memory database. The testing tasks will comprise running queries and plugging the interface to one of the implementations of the AMIE algorithm. We will measure the overhead caused by the remote communication. 
  • Release a 0.1 version of the in-memory database
  • Profile and optimize the HiPaR algorithm
  • Release a 0.3 version of the HiPaR algorithm

We plan to conduct the activities in a sequential way. That is, we will start with one of the tasks first and move on to the next one when we are done.

Additional activities (3 maximum) :

  • If the candidate is interested in conducting some research on any of the topics, they can always talk to us to define a scope of experimentation in close relation to the aforementioned engineering activities.



Technical skills and level required :

  • Ability to code in Python and scikit learn
  • Ability to code either in Java or in Rust
  • Basic knowledge of Machine Learning

Languages :

  • English: ability to read and understand scientific articles written in English



  • Subsidized meals
  • Partial reimbursement of public transport costs
  • Possibility of teleworking (90 days per year) and flexible organization of working hours
  • Partial payment of insurance costs


monthly gross salary from 2655 euros according to diploma and experience