Machine Learning Engineer

1000scholars; Paris; On-site; EUR 55,000 - 75,000; Full time

Job summary

1000scholars is seeking a Machine Learning Engineer in France to design and develop a Python software library for evaluating theoretical generalization bounds in deep learning. The role involves bridging theory and practice by creating a modular, efficient framework to compute and compare bounds on modern neural networks.

The candidate will conduct experiments, prepare software libraries, write scientific articles, and share results at conferences and venues, with potential travel within grant

Qualifications

• Strong Python programming skills and experience with PyTorch.
• Solid understanding of machine learning and deep learning.
• Experience in scientific software development, testing, and version control.
• Familiarity with statistical learning theory and generalization bounds is a plus.
• Ability to work collaboratively with researchers and translate mathematical concepts into reliable software.

Responsibilities

• Conduct experiments for empirical verification
• Prepare software libraries
• Write scientific articles
• Disseminate the scientific work in appropriate venues.

Skills

Python

PyTorch

Machine Learning

Deep Learning

Testing

Version control

Statistical learning theory

Generalization bounds

Research collaboration

Tools

Git

NumPy

Job description

Context

The position will be in the framework of the ERC Starting Grant DYNASTY (Dynamics-Aware Theory of Deep Learning). The position might include traveling to conferences for paper presentation. Travel expenses will be covered within the limits of the scale in force.

Assignment

We are seeking a Machine Learning Engineer to design and develop a Python software library for evaluating theoretical generalization bounds in deep learning. The objective is to bridge the gap between theoretical advances in learning theory and practical deep learning by providing a modular, efficient, and user-friendly framework for computing and comparing generalization bounds on modern neural networks.

Main activities

• Conduct experiments for empirical verification
• Prepare software libraries
• Write scientific articles
• Disseminate the scientific work in appropriate venues.

Skills

• Strong Python programming skills and experience with PyTorch.
• Solid understanding of machine learning and deep learning.
• Experience in scientific software development, testing, and version control.
• Familiarity with statistical learning theory and generalization bounds is a plus.
• Ability to work collaboratively with researchers and translate mathematical concepts into reliable software.