About the Role

From the Data Engineering team, you will work on ambitious and critical projects for the

company such as:

• The data access service

• The ML inference service

• Data pipelines including ML pipeline, cold and live data ingestions

You will occupy a central position in the model development lifecycle, writing high-quality

production code for our inference server, aligning closely with the product and platform

teams and designing automated evaluation pipelines to continuously assess and improve

performance.

You will be responsible for building scalable pipeline and help reduce build time for new

clients.

QualificationsRequired qualifications:

• 3+ years of experience in data engineering

• Experience in dealing with the machine learning model lifecycle, ideally, the

candidate has experience on serving machine learning models

• Strong background in data orchestration (Airflow or Dagster)

• Excellent communication and documentation skills

• Fluent English (French is not required)

• Python, Airflow, Docker

Nice to have:

• Experience in managing REST APIs

• Ray, Dagster, Github Actions, K8S, Python ML and scientific libraries (pytorch,

numpy, sklearn, pandas)

• Experience in healthcare or insurance ML applications

• Apache Spark, multithreaded, parallel and distributed calculation experience

Recruitment process

• Fit interview (30min): a first discussion to tell you more about the company and

understand your background

• Live coding interview (45-60min): Check your programming skills around an

algorithmic exercize

• System Design Interview (45-60min): Assess your ability to design scalable and

maintainable ML pipelines