Internship details

• Contract: Apprenticeship

• Location: Toulouse

• Team: MLOps

• Internship Tutor: Nathan Labbe

• Apprentice salary, “tickets restaurant” Swile, “CSE”

Mission

EasyMile has new projects and the R&D team is growing! Therefore we are looking for our future colleague to help us increase our features. You will join a team of >80 R&D engineers at the cutting edge of autonomous navigation technology, and work in a modern & open source environment.

You will join our amazing MLOps team and work on machine learning infrastructure and pipelines to scale our current data workflow and make our autonomous vehicles safer and smarter than ever.

Your future responsibilities

In collaboration with our Machine Learning Ops and our Data teams, you will participate to put into production DL based autonomous feature (New localization modality). To do so, you will :

• Define and implement pipelines / workflows for:

  • Training, validation, and optimization of machine learning based algorithms

  • Data gathering, versioning, preparation

  • Model Versioning, deployment, monitoring 

• Develop, construct and optimize machine learning based infrastructure(s) (e.g. databases, clusterGPU training server(s))

• Shape EasyMile’s data platform by ingesting, manipulating, and visualising data across data platforms

There is no typical profile at EasyMile, we all come from different backgrounds and that is what makes us strong! Don’t hesitate to apply if you are motivated and interested by innovative transportation and technologies.

We are looking for apprentice for 12 months or 24 months

Essentials 

• Master / Engineering School with Data engineering, machine learning background or similar

• Experience as a Data Engineer, Machine Learning Engineer, or similar.

• Experience with Python development

• Experience working with container technology including Docker 

• Experience working with cloud-based infrastructure (AWS)

• Good oral and written English

Nice to have 

• Experience with KubeFlow/Argo or similar (ML pipeline orchestration)

• Experience with MLFlow or tracking tool/model registry

• Knowledge in Kubernetes

• Familiar with deep learning algorithms (CNN, etc.)

• Passion for learning new technologies.

• 30 minutes call with a recruitment officer

• Meeting with the tutor, technical tests 

• One hour interview with the manager and a recruitment officer