Efficient And Robust Benchmarking For ai With Benchopt H/F - INRIA

A propos d'Inria

Inria est l'institut national de recherche dédié aux sciences et technologies du numérique. Il emploie 2600 personnes. Ses 215 équipes-projets agiles, en général communes avec des partenaires académiques, impliquent plus de 3900 scientifiques pour relever les défis du numérique, souvent à l'interface d'autres disciplines. L'institut fait appel à de nombreux talents dans plus d'une quarantaine de métiers différents. 900 personnels d'appui à la recherche et à l'innovation contribuent à faire émerger et grandir des projets scientifiques ou entrepreneuriaux qui impactent le monde. Inria travaille avec de nombreuses entreprises et a accompagné la création de plus de 200 start-up. L'institut s'eorce ainsi de répondre aux enjeux de la transformation numérique de la science, de la société et de l'économie.Efficient and robust benchmarking for AI with benchopt
Le descriptif de l'offre ci-dessous est en Anglais
Type de contrat : CDD

Contrat renouvelable : Oui

Niveau de diplôme exigé : Thèse ou équivalent

Fonction : Ingénieur scientifique contractuel

A propos du centre ou de la direction fonctionnelle

The Inria Saclay-Île-de-France Research Centre was established in 2008. It has developed as part of the Saclay site in partnership withParis-Saclay Universityand with theInstitut Polytechnique de Paris.

The centre has 40project teams, 27 of which operate jointly with Paris-Saclay University and the Institut Polytechnique de Paris; Its activities occupy over 600 people, scientists and research and innovation support staff, including 44 different nationalities.

Contexte et atouts du poste

While artificial intelligence is growing at a fast pace, the bulk of the world's computing power remains targeted at modeling and predicting physical phenomena, such as climate models, weather forecasting, or nuclear physics. These simulations are run on highly parallel supercomputers on which both the hardware and the software are optimized for the task at hand. While the computing power of each processing unit is still increasing, the communication networks and the storage capabilities in these clusters do not follow such fast trends. As a result, computing nodes produce outputs faster than what can be stored or sent to process elsewhere: These simulations are IO bound.
To reduce the storage and communication burden, a promising venue is in situ computations, meaning that most of the data is processed locally by the nodes, and only meaningful aggregates are stored or sent over the network. However, this is a difficult problem since meaningful information for the global simulation depends on the other nodes' output. This engineering position will aim at advancing distributed computation tools for ML libraries, in order to allow working with large distributed data.

Mission confiée

The candidate will both contribute to the joblib ecosystem, by participating to the maintenance and by adding new featues in joblib, cloudpickle, loky and other libraries for distributed computing. A particular focus will be put in ensuring compatibility with various AI libraries like torch and scikit-learn. In particular, for joblib:

- Improve caching capabilities in large distributed systems.
- Improve compatibilities with the array-API.
- Improve customizability to allow for extended experimentation with pluggin systems.
- Improve hashing and serialization for torch objects.

Principales activités

Main activities:

- Participate in the development of the team's open source software joblib and its ecosystem
- Improve tools to leverage large scale clusters with ML tools.

Additional activity: Participate to the team's research by providing support on how to parallelize reference benchmarks.

Compétences

- Good mathematical background. Knowledge in machine learning is a plus.
- Strong programming skills in Python. Knowledge of a deep learning framework is a plus.
- The candidate should be proficient in English. Knowing French is not necessary, as daily communication in the team is mostly in English due to the strong international environment.

Avantages

- - 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

Rémunération

According to profile